En la actualidad, la cantidad de artículos publicados en Internet está generando una gran ola de información accesible por cualquier usuario, dando a conocer diferentes puntos de vista, opiniones, información e investigaciones sobre diferentes temas de interés.
Esta gran cantidad de información no solo permite una búsqueda exhaustiva sobre un tema, también permite realizar un análisis sobre la tendencia de los diferentes temas que estén dando de qué hablar en una sociedad. Es por ello que un grupo de expertos se ha dado la tarea de analizar 10.000 artículos web y clasificarlos para poder establecer un análisis de los temas en la actualidad.
Para ello, como experto en análisis con machine learning, le han pedido que construya un modelo capaz de clasificar los nuevos artículos, realice un análisis de cuáles son los temas que dan de que hablar y automatice el proceso de selección y búsqueda de diferentes artículos.
Objetivos de desarrollo:
Datos: La fuente de los datos la puedes encontrar en News Articles Classification Dataset for NLP & ML.
Para tener un mejor detalle sobre el comportamiento de las variables, solicitamos a la organización el diccionario de datos y nos suministró la siguiente información:
| ATRIBUTO | DEFINICIÓN |
|---|---|
| headlines | Titular del artículo. |
| description | Reseña del artículo. |
| content | Contenido del artículo. |
| url | Dirección web del artículo. |
| category | Representa la temática del artículo. |
Realizar el análisis exploratorio de componentes principales en la información.
Identificar el número de componentes principales apropiado el procesamiento. Genera una tabla comparativa y los gráficos que apoyen este proceso. Recuerda que no deben truncarse los textos. Por último, la elección del número de componentes debe estar debidamente justificada.
Construir la red neuronal tomando como insumo los componentes principales procesados en el punto anterior.
Construir las gráficas de entrenamiento, validación. Debes interpretar los resultados obtenidos para este modelo base.
Realizar la identificación de hiperparámetros, justificando la elección de los valores correspondientes.
NOTA: La calificación será sobre notebook ejecutado y cargado en Bloque Neón junto con el archivo HTML.
gpu_info = !nvidia-smi
gpu_info = '\n'.join(gpu_info)
if gpu_info.find('failed') >= 0:
print('Not connected to a GPU')
else:
print(gpu_info)
Sun Apr 21 14:42:31 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 536.67 Driver Version: 536.67 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce GTX 1650 WDDM | 00000000:2B:00.0 On | N/A |
| 23% 34C P8 10W / 75W | 1338MiB / 4096MiB | 10% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 500 C+G ...on\123.0.2420.97\msedgewebview2.exe N/A |
| 0 N/A N/A 4704 C+G ...2txyewy\StartMenuExperienceHost.exe N/A |
| 0 N/A N/A 8348 C+G ...al\Discord\app-1.0.9042\Discord.exe N/A |
| 0 N/A N/A 8456 C+G C:\Windows\explorer.exe N/A |
| 0 N/A N/A 10048 C+G ...les\Microsoft OneDrive\OneDrive.exe N/A |
| 0 N/A N/A 13564 C+G ...CBS_cw5n1h2txyewy\TextInputHost.exe N/A |
| 0 N/A N/A 14620 C+G ...ekyb3d8bbwe\PhoneExperienceHost.exe N/A |
| 0 N/A N/A 16148 C+G ...crosoft\Edge\Application\msedge.exe N/A |
| 0 N/A N/A 16692 C+G ...63.0_x64__zpdnekdrzrea0\Spotify.exe N/A |
| 0 N/A N/A 18712 C+G ...Data\Local\Programs\Opera\opera.exe N/A |
| 0 N/A N/A 18912 C+G ...Programs\Microsoft VS Code\Code.exe N/A |
| 0 N/A N/A 21564 C+G ...ta\Local\Programs\Notion\Notion.exe N/A |
| 0 N/A N/A 21572 C+G ...500_x64__8wekyb3d8bbwe\ms-teams.exe N/A |
| 0 N/A N/A 25592 C+G ...\Local\slack\app-4.37.101\slack.exe N/A |
| 0 N/A N/A 25940 C+G ...siveControlPanel\SystemSettings.exe N/A |
| 0 N/A N/A 26260 C+G ....0_x64__kzh8wxbdkxb8p\DCv2\DCv2.exe N/A |
| 0 N/A N/A 28604 C+G ...on\123.0.2420.97\msedgewebview2.exe N/A |
| 0 N/A N/A 30264 C+G ...8.0_x64__cv1g1gvanyjgm\WhatsApp.exe N/A |
| 0 N/A N/A 31280 C+G ...les\Microsoft OneDrive\OneDrive.exe N/A |
| 0 N/A N/A 32236 C+G ...1.0_x64__8wekyb3d8bbwe\Video.UI.exe N/A |
| 0 N/A N/A 33744 C+G ...5n1h2txyewy\ShellExperienceHost.exe N/A |
| 0 N/A N/A 36592 C+G ...on\123.0.2420.97\msedgewebview2.exe N/A |
| 0 N/A N/A 37440 C+G ...\cef\cef.win7x64\steamwebhelper.exe N/A |
| 0 N/A N/A 41220 C+G ...oogle\Chrome\Application\chrome.exe N/A |
| 0 N/A N/A 41520 C+G ...nt.CBS_cw5n1h2txyewy\SearchHost.exe N/A |
+---------------------------------------------------------------------------------------+
!pip install ydata-profiling
Collecting ydata-profiling
Downloading ydata_profiling-4.7.0-py2.py3-none-any.whl (357 kB)
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Downloading htmlmin-0.1.12.tar.gz (19 kB)
Preparing metadata (setup.py) ... done
Collecting phik<0.13,>=0.11.1 (from ydata-profiling)
Downloading phik-0.12.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (686 kB)
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Downloading seaborn-0.12.2-py3-none-any.whl (293 kB)
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Downloading typeguard-4.2.1-py3-none-any.whl (34 kB)
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Downloading ImageHash-4.3.1-py2.py3-none-any.whl (296 kB)
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Downloading dacite-1.8.1-py3-none-any.whl (14 kB)
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Building wheels for collected packages: htmlmin
Building wheel for htmlmin (setup.py) ... done
Created wheel for htmlmin: filename=htmlmin-0.1.12-py3-none-any.whl size=27080 sha256=a096c7cf2f2e69731073480fc7f3ad908fda0e13f73c12a150bb3460ed41be70
Stored in directory: /root/.cache/pip/wheels/dd/91/29/a79cecb328d01739e64017b6fb9a1ab9d8cb1853098ec5966d
Successfully built htmlmin
Installing collected packages: htmlmin, typeguard, multimethod, dacite, imagehash, visions, seaborn, phik, ydata-profiling
Attempting uninstall: seaborn
Found existing installation: seaborn 0.13.1
Uninstalling seaborn-0.13.1:
Successfully uninstalled seaborn-0.13.1
Successfully installed dacite-1.8.1 htmlmin-0.1.12 imagehash-4.3.1 multimethod-1.11.2 phik-0.12.4 seaborn-0.12.2 typeguard-4.2.1 visions-0.7.6 ydata-profiling-4.7.0
!pip install kaggle
Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16) Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0) Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.2.2) Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2) Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.31.0) Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.2) Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4) Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.7) Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0) Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1) Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3) Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2) Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.6)
!pip install keras-tuner
Collecting keras-tuner
Downloading keras_tuner-1.4.7-py3-none-any.whl (129 kB)
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Requirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (from keras-tuner) (2.15.0)
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Collecting kt-legacy (from keras-tuner)
Downloading kt_legacy-1.0.5-py3-none-any.whl (9.6 kB)
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Installing collected packages: kt-legacy, keras-tuner
Successfully installed keras-tuner-1.4.7 kt-legacy-1.0.5
!pip install spacy
Requirement already satisfied: spacy in /usr/local/lib/python3.10/dist-packages (3.7.4) Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy) (3.0.12) Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy) (1.0.5) Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy) (1.0.10) Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy) (2.0.8) Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy) (3.0.9) Requirement already satisfied: thinc<8.3.0,>=8.2.2 in /usr/local/lib/python3.10/dist-packages (from spacy) (8.2.3) Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy) (1.1.2) Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy) (2.4.8) Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy) (2.0.10) Requirement already satisfied: weasel<0.4.0,>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from spacy) (0.3.4) Requirement already satisfied: typer<0.10.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from spacy) (0.9.4) Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from spacy) (6.4.0) Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/dist-packages (from spacy) (4.66.2) Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from spacy) (2.31.0) Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy) (2.6.4) Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy) (3.1.3) Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from spacy) (67.7.2) Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from spacy) (24.0) Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy) (3.3.0) Requirement already satisfied: numpy>=1.19.0 in /usr/local/lib/python3.10/dist-packages (from spacy) (1.25.2) Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy) (0.6.0) Requirement already satisfied: pydantic-core==2.16.3 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy) (2.16.3) Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy) (4.11.0) Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (3.3.2) Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (3.6) Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2.0.7) Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2024.2.2) Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.2.2->spacy) (0.7.11) Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.2.2->spacy) (0.1.4) Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer<0.10.0,>=0.3.0->spacy) (8.1.7) Requirement already satisfied: cloudpathlib<0.17.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from weasel<0.4.0,>=0.1.0->spacy) (0.16.0) Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy) (2.1.5)
#Librerías para identificación de idiomas
!pip install polyglot
!pip install PyICU
!pip install pycld2
Collecting polyglot
Downloading polyglot-16.7.4.tar.gz (126 kB)
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Building wheels for collected packages: polyglot
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Installing collected packages: polyglot
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Collecting PyICU
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Collecting pycld2
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!pip install contractions
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#Manejo de datos
import pandas as pd
import numpy as np
#Visualización de datos
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
#Analisis profundo de datos
from ydata_profiling import ProfileReport
#Entrenamiento del modelo
import sklearn
from sklearn.decomposition import PCA, TruncatedSVD
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import train_test_split
#from sklearn.metrics import mean_squared_error, r2_score
#from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import LabelEncoder
#from sklearn.compose import ColumnTransformer, make_column_selector
from sklearn.metrics import classification_report, confusion_matrix, PrecisionRecallDisplay
#Textos
import contractions
import nltk
import inflect
import re, string, unicodedata
from nltk import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import SnowballStemmer, WordNetLemmatizer
from wordcloud import WordCloud, STOPWORDS
#Tensorflow y keras
import tensorflow as tf
from keras.callbacks import EarlyStopping
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import plot_model
#Sistema operativo
import os
import os.path as osp
#Librerías extras
import itertools
from datetime import datetime
print(f"La versión de sklearn es: {sklearn.__version__}")
print(f'La versión de Tensor Flow es:', tf.__version__)
La versión de sklearn es: 1.4.2 La versión de Tensor Flow es: 2.16.1
Descarga de información de nltk
nltk.download('all')
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True
#Porcentaje para validación y test
test_alpha = 0.2
#random_state o semilla para la reproducibilidad
my_seed = 19
import os
import shutil
# Obtener la ruta del directorio del usuario
home_dir = os.path.expanduser("~")
# Crear el directorio .kaggle si no existe
kaggle_dir = os.path.join(home_dir, ".kaggle")
if not os.path.exists(kaggle_dir):
os.makedirs(kaggle_dir)
# Copiar el archivo kaggle.json al directorio .kaggle
src_file = "kaggle.json"
dst_file = os.path.join(kaggle_dir, src_file)
shutil.copy(src_file, dst_file)
# Cambiar los permisos del archivo kaggle.json para que solo el usuario tenga acceso de lectura
os.chmod(dst_file, 0o600)
print("Configuración de la API de Kaggle completada.")
Configuración de la API de Kaggle completada.
!kaggle datasets list
Warning: Looks like you're using an outdated API Version, please consider updating (server 1.6.12 / client 1.6.6) ref title size lastUpdated downloadCount voteCount usabilityRating --------------------------------------------------- -------------------------------------------------- ----- ------------------- ------------- --------- --------------- rahulvyasm/netflix-movies-and-tv-shows Netflix Movies and TV Shows 1MB 2024-04-10 09:48:38 3566 70 1.0 sudarshan24byte/online-food-dataset Online Food Dataset 3KB 2024-03-02 18:50:30 30576 587 0.9411765 nayanack/netflix Netflix Chronicles: Exploring Movies and TV Shows 1MB 2024-04-16 07:36:08 1096 23 0.88235295 mexwell/heart-disease-dataset 🫀 Heart Disease Dataset 399KB 2024-04-08 09:43:49 1876 33 1.0 asaniczka/university-employee-salaries-2011-present University Employee Salaries (2011 - Present) 17MB 2024-04-07 10:11:15 1572 45 1.0 akankshaaa013/top-grossing-movies-dataset Top Grossing Movies Dataset 33KB 2024-04-08 08:29:47 1584 35 1.0 prishasawhney/mushroom-dataset Mushroom Dataset (Binary Classification) 602KB 2024-04-18 19:56:44 462 46 1.0 fatemehmehrparvar/obesity-levels Obesity Levels 58KB 2024-04-07 16:28:30 3169 55 0.88235295 sukhmandeepsinghbrar/water-quality Water Quality 49MB 2024-04-19 07:53:13 329 32 1.0 sakshisatre/social-advertisement-dataset Social Media Consumer Buying Behavior Dataset 1KB 2024-04-14 08:47:43 942 30 1.0 sunnykakar/spotify-charts-all-audio-data Spotify Charts (All Audio Data) 3GB 2024-04-15 20:15:15 889 31 1.0 arnavsmayan/amazon-prime-userbase-dataset Amazon Prime Userbase Dataset 104KB 2024-04-15 06:25:10 1139 28 1.0 prishasawhney/good-reads-top-1000-books Good Reads Dataset (Top 1000 Books) 26KB 2024-04-17 20:02:53 266 44 1.0 sahirmaharajj/employee-salaries-analysis Employee Salaries Analysis 101KB 2024-03-31 16:32:47 2339 58 1.0 bhavikjikadara/student-study-performance Student Study Performance 9KB 2024-03-07 06:14:09 14401 178 1.0 anandshaw2001/customer-churn-dataset Customer Churn Dataset 262KB 2024-04-09 18:41:58 1100 27 1.0 startalks/pii-models pii-models 1GB 2024-03-21 21:23:40 181 24 1.0 sanyamgoyal401/customer-purchases-behaviour-dataset Customer Purchases Behaviour Dataset 1MB 2024-04-06 18:42:01 2264 46 1.0 varunraskar/cancer-regression Cancer Regression 339KB 2024-04-14 12:58:28 661 22 0.9411765 susanta21/student-attitude-and-behavior Student Attitude and Behavior 5KB 2024-04-13 12:16:32 1148 29 1.0
!kaggle datasets download banuprakashv/news-articles-classification-dataset-for-nlp-and-ml
news-articles-classification-dataset-for-nlp-and-ml.zip: Skipping, found more recently modified local copy (use --force to force download)
ROOT_DIR ='C:\\Users\\user\\BI-Sabroson\\Machine-Learning-Labs\\Talleres del Santi\\Taller 3'
DATASET_NAME = 'news-articles-classification-dataset-for-nlp-and-ml'
print(f"!unzip {DATASET_NAME}.zip -d {ROOT_DIR}/{DATASET_NAME}")
!unzip news-articles-classification-dataset-for-nlp-and-ml.zip -d C:\Users\user\BI-Sabroson\Machine-Learning-Labs\Talleres del Santi\Taller 3/news-articles-classification-dataset-for-nlp-and-ml
import zipfile
#imprimir directorio actual
print(os.getcwd())
# Cambiar al directorio ROOT_DIR
os.chdir(ROOT_DIR)
# Crear el directorio DATASET_NAME
dataset_dir = os.path.join(ROOT_DIR, DATASET_NAME)
os.makedirs(dataset_dir, exist_ok=True)
# Descomprimir el archivo DATASET_NAME.zip en el directorio DATASET_NAME
zip_file = os.path.join(ROOT_DIR, f"{DATASET_NAME}.zip")
with zipfile.ZipFile(f"{DATASET_NAME}.zip", 'r') as zip_ref:
zip_ref.extractall(dataset_dir)
print("Descompresión completada.")
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\Talleres del Santi\Taller 3 Descompresión completada.
DATA_DIR = f"{ROOT_DIR}/{DATASET_NAME}"
print(DATA_DIR)
C:\Users\user\BI-Sabroson\Machine-Learning-Labs\Talleres del Santi\Taller 3/news-articles-classification-dataset-for-nlp-and-ml
csv_files = os.listdir(DATA_DIR)
train_df = pd.DataFrame()
test_df = pd.DataFrame()
for csv_file in csv_files:
new_df = pd.read_csv(osp.join(DATA_DIR, csv_file))
train, test = train_test_split(new_df, test_size=test_alpha, random_state=my_seed)
train_df = pd.concat([train_df, train])
test_df = pd.concat([test_df, test])
train_df.head()
| headlines | description | content | url | category | |
|---|---|---|---|---|---|
| 636 | Gold Silver Rates Today: Precious metals price... | In Chennai, 24-carat gold per 10 gram was sell... | Gold Silver Rates Today (November 1): Precious... | https://indianexpress.com/article/business/com... | business |
| 161 | India’s forex reserves jump USD 2.75 bn to USD... | Gold reserves were up by USD 853 million to US... | India’s forex reserves jumped by USD 2.759 bil... | https://indianexpress.com/article/business/ind... | business |
| 855 | From capital to people, a lot at stake for Ind... | Over 1,600 people have been killed so far sinc... | A wide range of Indian businesses are closely ... | https://indianexpress.com/article/business/fro... | business |
| 24 | Q3 Results: IOC, DLF, Bajaj Auto, TVS Motor re... | Q3 Results: Most companies managed their perfo... | Investors will continue their focus on earning... | https://indianexpress.com/article/business/com... | business |
| 252 | India says IMF debt warning a worst case scenario | The IMF, in a so-called article IV review, sai... | The Indian government said on Friday a warning... | https://indianexpress.com/article/business/eco... | business |
A continuación se muestra la separación de los datos en ambos sets de entrenamiento y de evaluación.
train_count = train_df.shape[0]
test_count = test_df.shape[0]
print("-------------------SEPARACIÓN DE LA INFORMACIÓN-------------------")
print(f"-> Train: {train_count:,}")
print(f"-> Test: {test_count:,}")
-------------------SEPARACIÓN DE LA INFORMACIÓN------------------- -> Train: 8,000 -> Test: 2,000
Como se puede observar, el dataset original parece contener 10000 datos, separados en una proporción 80% train y 20% test
Es necesario revisar que tan balanceadas están las categorías tanto para los datos de entrenamiento como para los de evaluación.
plt.title("Categorias en set de entrenamiento")
train_df.groupby('category').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))
plt.gca().spines[['top', 'right',]].set_visible(False)
plt.title("Categorias en set de evaluación")
test_df.groupby('category').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))
plt.gca().spines[['top', 'right',]].set_visible(False)
Luego de graficar ambos datasets es posible decir que en ambos casos las clases se encuentran balanceadas y que cuentan con 5 diferentes tipos de categorías: tecnología, deportes, entretenimiento, educación y negocio.
Se definen las variables a utilizar para la red neuronal.
target_feature = 'category'
x_feature = 'content'
Se utilizará WordCloud para poder visualizar las palabras más recurrentes dentro de el conjunto de datos.
def show_wordcloud(palabras,stopwords=[]):
comment_words = ''
# iterate through the csv file
for val in palabras:
# typecaste each val to string
val = str(val)
# split the value
tokens = val.split()
# Converts each token into lowercase
for i in range(len(tokens)):
tokens[i] = tokens[i].lower()
comment_words += " ".join(tokens)+" "
wordcloud = WordCloud(width = 800, height = 800,
background_color ='white',
stopwords = stopwords,
min_font_size = 10).generate(comment_words)
# plot the WordCloud image
plt.figure(figsize = (8, 8), facecolor = None)
plt.imshow(wordcloud)
plt.axis("off")
plt.tight_layout(pad = 0)
plt.show()
for i in train_df[target_feature].unique():
print(f'---------- Words for class: {i} ----------')
show_wordcloud(train_df.loc[train_df[target_feature]==i, x_feature])
---------- Words for class: business ----------
---------- Words for class: education ----------
---------- Words for class: entertainment ----------
---------- Words for class: sports ----------
---------- Words for class: technology ----------
Después de realizar la visualización de los datos, se evidencia la necesidad de eliminar las stopwords, palabras comunes que no añaden significado al análisis de texto. La librería NLTK provee herramientas para este fin, permitiendo filtrar palabras según una lista predefinida de stopwords. Esto mejora la calidad del análisis al enfocarse en palabras relevantes, como sustantivos y adjetivos, facilitando la extracción de información importante del texto.
Sin embargo, como las stopwords son altamente sensibles al idioma del texto que se va a tratar, hay que revisar que todos los datos se encuentren en inglés.
*Esto se revisó desde Google Colab pero tuvo que quitarse al pasarse a local debido a problemas con la librería Polyglot y que en Colab se acabaron los creditos para usar la GPU *
Se confirma que todos los datos están en inglés y por lo tanto se pueden eliminar las stopwords con la seguridad de que será hecho de manera efectiva para el idioma inglés.
stop_words = stopwords.words('english')
for i in train_df[target_feature].unique():
print(f'---------- Words for class: {i} ----------')
show_wordcloud(train_df.loc[train_df[target_feature]==i, x_feature], stop_words)
---------- Words for class: business ----------
---------- Words for class: education ----------
---------- Words for class: entertainment ----------
---------- Words for class: sports ----------
---------- Words for class: technology ----------
Dentro del preprocesamiento de los datos se han decidido realizar las siguientes cuatro acciones:
Normalización
Estas transformaciones permiten que los datos queden en un formato estructurado y listo para ser procesados por la red neuronal, cumpliendo con estándares de calidad como la completitud, la consistencia, la exactitud y la relevancia. Además, estas acciones facilitan el aprendizaje de la red neuronal, mejoran la precisión, reducen el tiempo de entrenamiento y aumentan la generalización, lo que se traduce en un mejor rendimiento y una mayor confiabilidad del modelo de aprendizaje automático.
Es crucial que las categorías se representen de manera numérica en análisis de datos y aprendizaje automático, ya que muchos algoritmos requieren datos numéricos para operar de manera efectiva. Cuando las categorías están en formato no numérico, como texto o cadenas, es necesario transformarlas a valores numéricos para poder utilizarlas en modelos predictivos. Esta transformación es esencial para garantizar que el modelo pueda interpretar y aprender de los datos correctamente.
label_encoder = LabelEncoder()
train_df[target_feature] = label_encoder.fit_transform(train_df[target_feature])
test_df[target_feature] = label_encoder.fit_transform(test_df[target_feature])
unique_labels = label_encoder.classes_
for num_value, original_label in enumerate(unique_labels):
print(f'Valor numérico: {num_value}, Etiqueta original: {original_label}')
Valor numérico: 0, Etiqueta original: business Valor numérico: 1, Etiqueta original: education Valor numérico: 2, Etiqueta original: entertainment Valor numérico: 3, Etiqueta original: sports Valor numérico: 4, Etiqueta original: technology
X_train, Y_train = train_df[x_feature], train_df[target_feature]
X_test, Y_test = test_df[x_feature], test_df[target_feature]
display(X_train)
Y_train
636 Gold Silver Rates Today (November 1): Precious...
161 India’s forex reserves jumped by USD 2.759 bil...
855 A wide range of Indian businesses are closely ...
24 Investors will continue their focus on earning...
252 The Indian government said on Friday a warning...
...
936 Apart from stopping yourself from clicking on ...
1378 Japan, which had to put off the launch of its ...
757 Apple has been testing iOS 17.2 for quite a wh...
622 Following the success of the Chandrayaan-3 mis...
1629 Hot Jupiters are curious cosmic bodies. They a...
Name: content, Length: 8000, dtype: object
636 0
161 0
855 0
24 0
252 0
..
936 4
1378 4
757 4
622 4
1629 4
Name: category, Length: 8000, dtype: int32
La eliminación de ruido es fundamental al trabajar en la clasificación de textos, ya que permite mejorar la precisión y eficacia del modelo. Al filtrar información irrelevante, como palabras vacías, errores ortográficos o caracteres especiales, se optimiza el procesamiento de los datos, lo que conduce a una mejor comprensión del contenido. Además, al reducir la interferencia de factores externos, como el ruido ambiental o la variabilidad en la forma de expresión, se incrementa la capacidad del modelo para identificar patrones significativos y tomar decisiones más precisas en la clasificación de textos.
def remove_non_ascii(words):
"""Remove non-ASCII characters from list of tokenized words"""
new_words = []
for word in words:
new_word = unicodedata.normalize('NFKD', word).encode('ascii', 'ignore').decode('utf-8', 'ignore')
new_words.append(new_word)
return new_words
def to_lowercase(words):
"""Convert all characters to lowercase from list of tokenized words"""
new_words = []
for word in words:
new_word = word.lower()
new_words.append(new_word)
return new_words
def remove_punctuation(words):
"""Remove punctuation from list of tokenized words"""
new_words = []
for word in words:
new_word = re.sub(r'[^\w\s]', '', word)
if new_word != '':
new_words.append(new_word)
return new_words
def replace_numbers(words):
"""Replace all interger occurrences in list of tokenized words with textual representation"""
p = inflect.engine()
new_words = []
for word in words:
if word.isdigit():
new_word = p.number_to_words(word)
new_words.append(new_word)
else:
new_words.append(word)
return new_words
def remove_stopwords(words, stopwords=stopwords.words('english')):
"""Remove stop words from list of tokenized words"""
new_words = []
for word in words:
if word not in stopwords:
new_words.append(word)
return new_words
def preproccesing(words):
words = to_lowercase(words)
words = replace_numbers(words)
words = remove_punctuation(words)
words = remove_non_ascii(words)
words = remove_stopwords(words)
return words
La tokenización es un paso crucial en el procesamiento de texto que ofrece diversas ventajas al trabajar en la clasificación de textos. Esta transformación facilita el análisis y la extracción de características relevantes para la clasificación. Esto permite una representación más estructurada y uniforme del texto, lo que a su vez mejora la capacidad del modelo para capturar la semántica y el contexto.
X_train_new = X_train.apply(word_tokenize)
X_train_new = X_train_new.apply(preproccesing) #Aplica la eliminación del ruido
X_train_new.head()
636 [gold, silver, rates, today, november, one, pr... 161 [india, forex, reserves, jumped, usd, 2759, bi... 855 [wide, range, indian, businesses, closely, mon... 24 [investors, continue, focus, earning, wednesda... 252 [indian, government, said, friday, warning, in... Name: content, dtype: object
X_train_trans = X_train_new.copy()
X_train_trans['token_count'] = X_train_trans.apply(lambda x: len(x))
X_train_trans['token_count'].mean()
134.212625
Hay un promedio de 134.21 tokens por cada registro en el conjunto de datos.
La normalización en el procesamiento de texto involucra técnicas como Stemming y Lemmatizing, que son fundamentales cuando se quiere que un modelo analice textos con muchas palabras únicas. El Stemming reduce las palabras a su raíz, simplificando la representación y reduciendo la dimensionalidad del espacio de características. Por otro lado, el Lemmatizing va más allá al reducir las palabras a su forma base, considerando la morfología y la gramática del idioma para proporcionar una representación más precisa y coherente del texto. Juntos, estos enfoques optimizan la calidad y eficiencia de los modelos de clasificación al mejorar la coherencia y la representación del texto.
def stem_words(words):
"""Stem words in list of tokenized words"""
stemmer = SnowballStemmer('english')
stems = []
for word in words:
stem = stemmer.stem(word)
stems.append(stem)
return stems
def lemmatize_verbs(words):
"""Lemmatize verbs in list of tokenized words"""
lemmatizer = WordNetLemmatizer()
lemmas = []
for word in words:
lemma = lemmatizer.lemmatize(word, pos='v')
lemmas.append(lemma)
return lemmas
def stem_and_lemmatize(words):
words = stem_words(words)
words = lemmatize_verbs(words)
return words
X_train_new = X_train_new.apply(stem_and_lemmatize) #Aplica lematización y Eliminación de Prefijos y Sufijos.
X_train_new.head()
636 [gold, silver, rate, today, novemb, one, preci... 161 [india, forex, reserv, jump, usd, 2759, billio... 855 [wide, ring, indian, busi, close, monitor, ong... 24 [investor, continu, focus, earn, wednesday, se... 252 [indian, govern, say, friday, warn, intern, mo... Name: content, dtype: object
Se actualiza el conjunto de datos de entrenamiento
train_df['trans'] = X_train_new.apply(lambda x: ' '.join(map(str, x)))
train_df.head()
| headlines | description | content | url | category | trans | |
|---|---|---|---|---|---|---|
| 636 | Gold Silver Rates Today: Precious metals price... | In Chennai, 24-carat gold per 10 gram was sell... | Gold Silver Rates Today (November 1): Precious... | https://indianexpress.com/article/business/com... | 0 | gold silver rate today novemb one precious met... |
| 161 | India’s forex reserves jump USD 2.75 bn to USD... | Gold reserves were up by USD 853 million to US... | India’s forex reserves jumped by USD 2.759 bil... | https://indianexpress.com/article/business/ind... | 0 | india forex reserv jump usd 2759 billion usd 6... |
| 855 | From capital to people, a lot at stake for Ind... | Over 1,600 people have been killed so far sinc... | A wide range of Indian businesses are closely ... | https://indianexpress.com/article/business/fro... | 0 | wide ring indian busi close monitor ongo confl... |
| 24 | Q3 Results: IOC, DLF, Bajaj Auto, TVS Motor re... | Q3 Results: Most companies managed their perfo... | Investors will continue their focus on earning... | https://indianexpress.com/article/business/com... | 0 | investor continu focus earn wednesday sever he... |
| 252 | India says IMF debt warning a worst case scenario | The IMF, in a so-called article IV review, sai... | The Indian government said on Friday a warning... | https://indianexpress.com/article/business/eco... | 0 | indian govern say friday warn intern monetari ... |
La técnica Term Frequency-Inverse Document Frequency (TF-IDF) es crucial en la clasificación de textos debido a su capacidad para resaltar la importancia relativa de las palabras en un documento dentro de un corpus más amplio. La frecuencia de término (TF) mide la relevancia de una palabra en un documento específico, mientras que la inversa de la frecuencia del documento (IDF) evalúa la rareza de un término en el conjunto de documentos. Esta técnica reduce la influencia de palabras comunes y resalta aquellas que son más descriptivas y específicas del contenido del documento, lo que mejora la capacidad del modelo para capturar la semántica y el contexto en la clasificación de textos.
tfidf_vect = TfidfVectorizer()
X_train_new_v = X_train_new.apply(lambda words: ' '.join(words))
X_tfidf = tfidf_vect.fit_transform(X_train_new_v)
terms = tfidf_vect.get_feature_names_out()
print(f"El número de columnas es: {len(terms)}")
terms
tfidf_df = pd.DataFrame(X_tfidf.toarray(), columns=terms)
tfidf_df
El número de columnas es: 43713
| 00 | 000 | 001 | 002 | 003 | 004 | 005 | 006 | 007 | 008 | ... | zuckerberg | zuckerbergl | zulfon | zulili | zulkifli | zurich | zve10 | zverev | zwischenahn | zyada | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 7995 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 7996 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 7997 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 7998 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 7999 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
8000 rows × 43713 columns
Claramente tener 43713 columnas no es óptimo para lograr un modelo que generalice de manera correcta.
La razón para usar PCA en este contexto es que ayuda a visualizar la estructura de los datos y a detectar patrones o agrupaciones. Fijar un número de componentes permite controlar cuánta información se conserva en la proyección. Al fijar un número de componentes, se reduce la dimensionalidad del espacio de características, lo que facilita la visualización y la interpretación de los datos. Sin embargo, es importante elegir un número adecuado de componentes para evitar perder demasiada información o introducir ruido innecesario en la representación.
#La función grafica el último de los componentes identificados con sus respectivas clases
def draw_components(labels, X, Y, n_components):
# Inicializar LSA (TruncatedSVD), similar a PCA pero para matrices dispersas
pca = TruncatedSVD(n_components=n_components)
if n_components < 2:
raise("El número de componentes no puede ser menor a 2")
# Ajustar y transformar los datos TF-IDF
X_pca = pca.fit_transform(X)
print(X_pca.shape)
print("Varianza explicada: ", sum(pca.explained_variance_ratio_))
#Paleta de colores
colors = plt.cm.viridis(np.linspace(0, 1, len(labels)))
label_color_dict = dict(zip(labels, colors))
# Asignar un color a cada etiqueta
label_colors = [label_color_dict[label_encoder.inverse_transform([label])[0]] for label in Y]
# Gráfico
plt.figure(figsize=(10, 7))
scatter = plt.scatter(X_pca[:, 0], X_pca[:, n_components-1], c=label_colors, alpha=0.5)
#Leyenda
handles = [plt.Line2D([0], [0], marker='o', color=color, linewidth=0, markersize=10) for label, color in label_color_dict.items()]
plt.legend(handles, labels, title='Leyenda')
plt.show()
draw_components(unique_labels, tfidf_df, Y_train, 2)
(8000, 2) Varianza explicada: 0.016128159076136343
draw_components(unique_labels, tfidf_df, Y_train, 20)
(8000, 20) Varianza explicada: 0.09793489887718093
draw_components(unique_labels, tfidf_df, Y_train, 100)
(8000, 100) Varianza explicada: 0.2087917726315192
draw_components(unique_labels, tfidf_df, Y_train, 1000)
(8000, 1000) Varianza explicada: 0.5718193261774015
draw_components(unique_labels, tfidf_df, Y_train, 8000)
(8000, 8000) Varianza explicada: 1.0000000000000056
Como se puede observar, al sacar 8000 componentes principales, la varianza explicada por todos los componentes obtenidos es del 100%, como la idea es reducir al máximo el número de componentes principales se está intentando encontrar el mínimo número de componentes principales que permitirá tener por lo menos un 95% de la variabilidad explicada.
Dicho eso, es claro que se puede disminuir un poco más el número de componentes principales.
draw_components(unique_labels, tfidf_df, Y_train, 6000)
(8000, 6000) Varianza explicada: 0.9742005013190539
Ya se redujo en 2000 componentes y todavía se explica el 97% de la variabilidad, es decir que todavía se puede reducir un poco más.
draw_components(unique_labels, tfidf_df, Y_train, 5000)
(8000, 5000) Varianza explicada: 0.945928984592642
Parece ser que la explicación de la varianza está muy cerca del 95%, sin embargo como está por debajo hay que aumentar ligeramente la cantidad de componentes principales.
draw_components(unique_labels, tfidf_df, Y_train, 5100)
(8000, 5100) Varianza explicada: 0.9493399646059367
draw_components(unique_labels, tfidf_df, Y_train, 3500)
(8000, 3500) Varianza explicada: 0.8739307706991458
draw_components(unique_labels, tfidf_df, Y_train, 3000)
(8000, 3000) Varianza explicada: 0.8384164985157284
draw_components(unique_labels, tfidf_df, Y_train, 2500)
(8000, 2500) Varianza explicada: 0.7946439094417896
draw_components(unique_labels, tfidf_df, Y_train, 2000)
(8000, 2000) Varianza explicada: 0.7397876404584564
draw_components(unique_labels, tfidf_df, Y_train, 1700)
(8000, 1700) Varianza explicada: 0.6995880954399445
| Cantidad de Componentes Principales | Explicabilidad de Varianza |
|---|---|
| 2 | 1.6% |
| 20 | 9.8% |
| 100 | 20.9% |
| 1000 | 57.2% |
| 1700 | 69.9% |
| 2000 | 73.9% |
| 2500 | 79.4% |
| 3000 | 83.8% |
| 3500 | 87.4% |
| 5000 | 94.6% |
| 5100 | 94.9% |
| 6000 | 97.4% |
| 8000 | 100% |
Luego de revisar los resultados de la tabla.Es posible observar que aunque inicialmente se logró explicar el 95% de la variabilidad utilizando 5100 columnas, pero es posible que reducir ligeramente la explicabilidad podría eliminar el ruido y mejorar la capacidad de generalización del modelo. Por lo tanto, se planea explorar diferentes niveles de explicabilidad, como conformarse solo con 80% o incluso el 70%, mediante la reducción del número de componentes. Para determinar el mejor equilibrio entre el número de componentes y la precisión del modelo, se tiene la intención de implementar tres algoritmos base. Este enfoque permitirá identificar el punto óptimo que maximice la capacidad de generalización y la precisión del modelo, al tiempo que reduce la dimensionalidad y mitigar el impacto del ruido en los datos iniciales. De esta manera, se ha encontrado el grupo de números de componentes principales para realizar los algoritmos bases: 1700, 3000 y 5100.
A continuación se creará la clase que contendrá todos los preprocesamientos necesarios.
Importante: El código implementado abajo realiza un proceso de preprocesamiento de texto seguido de una transformación TF-IDF y una reducción de dimensionalidad mediante PCA para preparar los datos para su alimentación a una red neuronal.
Para garantizar la consistencia en la dimensionalidad de entrada de la red neuronal, se utiliza la técnica de padding con ceros, lo que asegura que todas las muestras tengan la misma dimensión, independientemente del tamaño del conjunto de datos (Como pasa en el caso de el conjunto de test). Esta decisión se justifica tanto por la necesidad de mantener la coherencia en los datos durante el entrenamiento de la red como por la arquitectura de la red neuronal, que requiere una dimensión de entrada uniforme para un procesamiento eficiente. Esto asegura que la red pueda entrenarse de manera efectiva y que los datos se procesen de manera uniforme, lo que contribuye a un mejor rendimiento del modelo.
class TextPreprocessing:
def __init__(self,stopwords=stopwords.words('english')):
self.stopwords = stopwords
self.max_words = 10000
self.tfidf_vect = TfidfVectorizer(max_features=self.max_words)
self.pca = TruncatedSVD(n_components=100)
def remove_non_ascii(self, words):
"""Remove non-ASCII characters from list of tokenized words"""
new_words = []
for word in words:
new_word = unicodedata.normalize('NFKD', word).encode('ascii', 'ignore').decode('utf-8', 'ignore')
new_words.append(new_word)
return new_words
def to_lowercase(self, words):
"""Convert all characters to lowercase from list of tokenized words"""
new_words = []
for word in words:
new_word = word.lower()
new_words.append(new_word)
return new_words
def remove_punctuation(self, words):
"""Remove punctuation from list of tokenized words"""
new_words = []
for word in words:
new_word = re.sub(r'[^\w\s]', '', word)
if new_word != '':
new_words.append(new_word)
return new_words
def replace_numbers(self, words):
"""Replace all interger occurrences in list of tokenized words with textual representation"""
p = inflect.engine()
new_words = []
for word in words:
if word.isdigit():
new_word = p.number_to_words(word)
new_words.append(new_word)
else:
new_words.append(word)
return new_words
def remove_stopwords(self, words):
"""Remove stop words from list of tokenized words"""
new_words = []
for word in words:
if word not in self.stopwords:
new_words.append(word)
return new_words
def stem_words(self, words):
"""Stem words in list of tokenized words"""
stemmer = SnowballStemmer('spanish')
stems = []
for word in words:
stem = stemmer.stem(word)
stems.append(stem)
return stems
def lemmatize_verbs(self, words):
"""Lemmatize verbs in list of tokenized words"""
lemmatizer = WordNetLemmatizer()
lemmas = []
for word in words:
lemma = lemmatizer.lemmatize(word, pos='v')
lemmas.append(lemma)
return lemmas
def stem_and_lemmatize(self, words):
words = self.stem_words(words)
words = self.lemmatize_verbs(words)
return words
def preproccesing(self, words):
words = self.to_lowercase(words)
words = self.replace_numbers(words)
words = self.remove_punctuation(words)
words = self.remove_non_ascii(words)
words = self.remove_stopwords(words)
return words
def transform(self,X, is_train, n_components):
X_train_new = pd.Series(X)
X_train_new = X_train_new.apply(contractions.fix)
X_train_new = X_train_new.apply(word_tokenize)
X_train_new = X_train_new.apply(lambda x: self.preproccesing(x))
X_train_new = X_train_new.apply(lambda x: self.stem_words(x))
X_train_new = X_train_new.apply(lambda x: ' '.join(map(str, x)))
if is_train:
X_tfidf = self.tfidf_vect.fit_transform(X_train_new)
self.pca = TruncatedSVD(n_components=n_components)
X_pca = self.pca.fit_transform(X_tfidf)
else:
X_tfidf = self.tfidf_vect.transform(X_train_new)
X_pca = self.pca.transform(X_tfidf)
return X_pca
Luego de construir la clase se creará la variable del pipeline.
pipeline5100 = TextPreprocessing()
pipeline3000 = TextPreprocessing()
pipeline1700 = TextPreprocessing()
Se aplica ahora el pipeline a la variable X_train y al X_test para los diferentes números de componentes
X_train_p_5100 = pipeline5100.transform(X_train, is_train=True, n_components=5100)
print(f"El tamaño es: {X_train_p_5100.shape}")
X_train_p_5100
El tamaño es: (8000, 5100)
array([[ 2.33922080e-01, 1.31247688e-01, 1.29150591e-01, ...,
3.83071873e-04, -1.81761921e-03, -3.80619541e-04],
[ 1.40489678e-01, 1.11609326e-01, 7.81257806e-02, ...,
9.36361213e-04, -1.61788771e-03, -6.02776232e-04],
[ 1.46370307e-01, 7.62418548e-02, 1.33796469e-02, ...,
-5.80696087e-03, 6.88744570e-03, -8.77516435e-03],
...,
[ 1.42712216e-01, -7.78335298e-02, 8.81215183e-03, ...,
1.58652025e-03, -1.96998086e-03, -8.10450624e-04],
[ 1.72146162e-01, 2.30874242e-02, -6.91681604e-02, ...,
-2.10605619e-04, -3.03561717e-03, 2.25113218e-03],
[ 8.74686615e-02, -6.41265008e-03, -2.18219202e-02, ...,
-2.89801995e-03, 1.93884474e-04, -8.69697973e-04]])
X_test_p_5100 = pipeline5100.transform(X_test, is_train=False, n_components=5100)
print(f"El tamaño es: {X_test_p_5100.shape}")
X_test_p_5100
El tamaño es: (2000, 5100)
array([[ 0.26466943, 0.38544188, 0.40960988, ..., 0.00614996,
0.00329459, 0.00368363],
[ 0.15139362, 0.05710875, 0.03571389, ..., 0.01565296,
0.0006136 , 0.01218145],
[ 0.18861376, 0.1519424 , 0.23431259, ..., -0.00617281,
-0.00086582, -0.00177212],
...,
[ 0.15161715, -0.02771074, -0.01228921, ..., -0.00545019,
0.00694453, 0.01365117],
[ 0.12271026, -0.00317327, -0.0028614 , ..., -0.00234666,
-0.01025236, -0.01235204],
[ 0.14617112, 0.01080924, -0.01822378, ..., -0.00671168,
-0.00253222, 0.01218919]])
X_train_p_3000 = pipeline3000.transform(X_train, is_train=True, n_components=3000)
print(f"El tamaño es: {X_train_p_3000.shape}")
X_train_p_3000
El tamaño es: (8000, 3000)
array([[ 0.23392208, 0.13124769, 0.12915059, ..., -0.0004145 ,
-0.00047416, 0.01585869],
[ 0.14048968, 0.11160933, 0.07812578, ..., 0.0026764 ,
0.00409704, 0.00084192],
[ 0.14637031, 0.07624185, 0.01337965, ..., -0.000559 ,
0.01381171, 0.00510932],
...,
[ 0.14271222, -0.07783353, 0.00881215, ..., -0.00060358,
0.00948086, 0.0144636 ],
[ 0.17214616, 0.02308742, -0.06916816, ..., -0.01642131,
0.00256817, 0.02176338],
[ 0.08746866, -0.00641265, -0.02182192, ..., 0.00211873,
-0.00214609, -0.00166762]])
X_test_p_3000 = pipeline3000.transform(X_test, is_train=False, n_components=5100)
print(f"El tamaño es: {X_test_p_3000.shape}")
X_test_p_3000
El tamaño es: (2000, 3000)
array([[ 2.64669427e-01, 3.85441877e-01, 4.09609879e-01, ...,
-2.89037672e-03, -4.27271768e-04, 6.61795876e-04],
[ 1.51393616e-01, 5.71087537e-02, 3.57138913e-02, ...,
-1.02057472e-02, 3.84510167e-03, 1.62568956e-03],
[ 1.88613757e-01, 1.51942400e-01, 2.34312586e-01, ...,
1.31085008e-03, -3.70271191e-03, 1.21968891e-02],
...,
[ 1.51617154e-01, -2.77107359e-02, -1.22892134e-02, ...,
-3.51868545e-03, -1.14618070e-02, -3.28454406e-04],
[ 1.22710257e-01, -3.17327322e-03, -2.86139573e-03, ...,
-9.40711336e-03, -2.08886355e-03, -1.16681814e-02],
[ 1.46171122e-01, 1.08092405e-02, -1.82237804e-02, ...,
-4.51740775e-03, -1.23300333e-03, -1.42111130e-02]])
X_train_p_1700 = pipeline1700.transform(X_train, is_train=True, n_components=1700)
print(f"El tamaño es: {X_train_p_1700.shape}")
X_train_p_1700
El tamaño es: (8000, 1700)
array([[ 0.23392208, 0.13124769, 0.12915059, ..., 0.00107393,
-0.00650558, 0.00246164],
[ 0.14048968, 0.11160933, 0.07812578, ..., -0.00098126,
0.00132311, 0.00319363],
[ 0.14637031, 0.07624185, 0.01337965, ..., 0.02128245,
0.00619049, -0.00525248],
...,
[ 0.14271222, -0.07783353, 0.00881215, ..., -0.01298731,
-0.00745993, 0.00362367],
[ 0.17214616, 0.02308742, -0.06916816, ..., 0.00741997,
0.00604218, 0.01136075],
[ 0.08746866, -0.00641265, -0.02182192, ..., 0.0284003 ,
0.0021205 , 0.00895434]])
X_test_p_1700 = pipeline1700.transform(X_test, is_train=False, n_components=5100)
print(f"El tamaño es: {X_test_p_1700.shape}")
X_test_p_1700
El tamaño es: (2000, 1700)
array([[ 0.26466943, 0.38544188, 0.40960988, ..., 0.00668125,
0.00325233, 0.00858644],
[ 0.15139362, 0.05710875, 0.03571389, ..., -0.00464378,
0.00317825, -0.00769993],
[ 0.18861376, 0.1519424 , 0.23431259, ..., 0.00513497,
0.00197944, 0.00369901],
...,
[ 0.15161715, -0.02771074, -0.01228921, ..., 0.01180813,
-0.00330885, -0.0015854 ],
[ 0.12271026, -0.00317327, -0.0028614 , ..., -0.00680957,
0.00095576, -0.00626612],
[ 0.14617112, 0.01080924, -0.01822378, ..., 0.006876 ,
-0.00098594, -0.00900724]])
A continuación se va a construir la red neuronal, la cual va a contar con una capa de entrada, una capa oculta y una capa de salida.
model = Sequential(name="Base_NN")
model.add(Dense(128, activation='relu', input_shape=(X_train_p_5100.shape[1],), name="Input_Layer"))
model.summary()
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
Model: "Base_NN"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ Input_Layer (Dense) │ (None, 128) │ 652,928 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 652,928 (2.49 MB)
Trainable params: 652,928 (2.49 MB)
Non-trainable params: 0 (0.00 B)
model.add(Dense(64, activation='relu', name="Hidden_Layer"))
model.summary()
Model: "Base_NN"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ Input_Layer (Dense) │ (None, 128) │ 652,928 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ Hidden_Layer (Dense) │ (None, 64) │ 8,256 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 661,184 (2.52 MB)
Trainable params: 661,184 (2.52 MB)
Non-trainable params: 0 (0.00 B)
model.add(Dense(len(unique_labels), activation="softmax", name='Output_Layer'))
model.summary()
Model: "Base_NN"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ Input_Layer (Dense) │ (None, 128) │ 652,928 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ Hidden_Layer (Dense) │ (None, 64) │ 8,256 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ Output_Layer (Dense) │ (None, 5) │ 325 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 661,509 (2.52 MB)
Trainable params: 661,509 (2.52 MB)
Non-trainable params: 0 (0.00 B)
Finalmente, se configuran las opciones de entrenamiento resantes. Se especifica el optimizador Adam para la actualización de los pesos del modelo durante el entrenamiento, la función de pérdida de entropía cruzada categórica dispersa para calcular la discrepancia entre las predicciones y las etiquetas verdaderas, y la métrica de precisión para evaluar el rendimiento del modelo.
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.summary()
Model: "Base_NN"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ Input_Layer (Dense) │ (None, 128) │ 652,928 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ Hidden_Layer (Dense) │ (None, 64) │ 8,256 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ Output_Layer (Dense) │ (None, 5) │ 325 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 661,509 (2.52 MB)
Trainable params: 661,509 (2.52 MB)
Non-trainable params: 0 (0.00 B)
Una vez configurada toda la red neuronal es posible entrenarla.
early_stopping = EarlyStopping(monitor='val_loss', patience=10, verbose=1, restore_best_weights=True)
with tf.device('/device:GPU:0'):
history = model.fit(X_train_p_5100, Y_train, validation_split=0.2, epochs=100, batch_size=32, verbose=2, callbacks=[early_stopping])
#Final metrics for accuracy in validation, print it
print(f"Accuracy: {history.history['accuracy'][-1]:.2f}")
print(f"Validation Accuracy: {history.history['val_accuracy'][-1]}")
#Final metrics for loss in validation, print it
print(f"Loss: {history.history['loss'][-1]:.2f}")
print(f"Validation Loss: {history.history['val_loss'][-1]}")
Epoch 1/100 200/200 - 1s - 5ms/step - accuracy: 0.8541 - loss: 0.7217 - val_accuracy: 0.0000e+00 - val_loss: 6.7149 Epoch 2/100 200/200 - 0s - 2ms/step - accuracy: 0.9994 - loss: 0.0115 - val_accuracy: 0.0000e+00 - val_loss: 7.4481 Epoch 3/100 200/200 - 0s - 2ms/step - accuracy: 1.0000 - loss: 0.0026 - val_accuracy: 0.0000e+00 - val_loss: 7.7607 Epoch 4/100 200/200 - 0s - 2ms/step - accuracy: 1.0000 - loss: 0.0013 - val_accuracy: 0.0000e+00 - val_loss: 7.9575 Epoch 5/100 200/200 - 0s - 2ms/step - accuracy: 1.0000 - loss: 7.7315e-04 - val_accuracy: 0.0000e+00 - val_loss: 8.1150 Epoch 6/100 200/200 - 0s - 2ms/step - accuracy: 1.0000 - loss: 5.1583e-04 - val_accuracy: 0.0000e+00 - val_loss: 8.2607 Epoch 7/100 200/200 - 0s - 2ms/step - accuracy: 1.0000 - loss: 3.6623e-04 - val_accuracy: 0.0000e+00 - val_loss: 8.3707 Epoch 8/100 200/200 - 0s - 2ms/step - accuracy: 1.0000 - loss: 2.7167e-04 - val_accuracy: 0.0000e+00 - val_loss: 8.4694 Epoch 9/100 200/200 - 0s - 2ms/step - accuracy: 1.0000 - loss: 2.0796e-04 - val_accuracy: 0.0000e+00 - val_loss: 8.5582 Epoch 10/100 200/200 - 0s - 2ms/step - accuracy: 1.0000 - loss: 1.6314e-04 - val_accuracy: 0.0000e+00 - val_loss: 8.6454 Epoch 11/100 200/200 - 0s - 2ms/step - accuracy: 1.0000 - loss: 1.3047e-04 - val_accuracy: 0.0000e+00 - val_loss: 8.7193 Epoch 11: early stopping Restoring model weights from the end of the best epoch: 1. Accuracy: 1.00 Validation Accuracy: 0.0 Loss: 0.00 Validation Loss: 8.719269752502441
with tf.device('/device:GPU:0'):
history = model.fit(X_train_p_3000, Y_train, validation_split=0.2, epochs=100, batch_size=32, verbose=2, callbacks=[early_stopping])
with tf.device('/device:GPU:0'):
history = model.fit(X_train_p_1700, Y_train, validation_split=0.2, epochs=100, batch_size=32, verbose=2, callbacks=[early_stopping])
Epoch 1/100 200/200 - 1s - 7ms/step - accuracy: 0.8597 - loss: 0.7085 - val_accuracy: 0.0000e+00 - val_loss: 6.6077 Epoch 2/100 200/200 - 0s - 2ms/step - accuracy: 0.9992 - loss: 0.0114 - val_accuracy: 0.0000e+00 - val_loss: 7.4789 Epoch 3/100 200/200 - 0s - 2ms/step - accuracy: 1.0000 - loss: 0.0025 - val_accuracy: 0.0000e+00 - val_loss: 7.7802 Epoch 4/100 200/200 - 0s - 2ms/step - accuracy: 1.0000 - loss: 0.0013 - val_accuracy: 0.0000e+00 - val_loss: 8.0498 Epoch 5/100 200/200 - 0s - 2ms/step - accuracy: 1.0000 - loss: 7.8322e-04 - val_accuracy: 0.0000e+00 - val_loss: 8.2592 Epoch 6/100 200/200 - 0s - 2ms/step - accuracy: 1.0000 - loss: 5.2835e-04 - val_accuracy: 0.0000e+00 - val_loss: 8.4279 Epoch 7/100 200/200 - 0s - 2ms/step - accuracy: 1.0000 - loss: 3.7839e-04 - val_accuracy: 0.0000e+00 - val_loss: 8.5764 Epoch 8/100 200/200 - 0s - 2ms/step - accuracy: 1.0000 - loss: 2.8230e-04 - val_accuracy: 0.0000e+00 - val_loss: 8.6823 Epoch 9/100 200/200 - 0s - 2ms/step - accuracy: 1.0000 - loss: 2.1724e-04 - val_accuracy: 0.0000e+00 - val_loss: 8.7902 Epoch 10/100 200/200 - 0s - 2ms/step - accuracy: 1.0000 - loss: 1.7095e-04 - val_accuracy: 0.0000e+00 - val_loss: 8.8772 Epoch 11/100 200/200 - 0s - 2ms/step - accuracy: 1.0000 - loss: 1.3688e-04 - val_accuracy: 0.0000e+00 - val_loss: 8.9616 Epoch 11: early stopping Restoring model weights from the end of the best epoch: 1.
Ahora que el modelo ha terminado de entrenarse es necesario visualizar el comportamiento de la red neuronal. En específico se graficará el valor de pérdida del modelo.
plt.plot(history.history['loss'], label='Train')
plt.plot(history.history['val_loss'], label='Val')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()
También se graficará el accuracy del modelo.
plt.plot(history.history['accuracy'], label='Train')
plt.plot(history.history['val_accuracy'], label='Val')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()
Tras obtener las gráficas de accuracy y de loss es posible observar que hay un sobreajuste bastante importante entre el train y el validate. Por como se comporta el modelo usando el set de entrenamiento es claro que la red neuronal es demasiado compleja y se "aprende" los datos. Los resultados del entrenamiento muestran una marcada discrepancia entre la precisión y la pérdida en los conjuntos de entrenamiento y validación. Aunque el modelo logra una precisión cercana al 100% en el conjunto de entrenamiento, este rendimiento no se traduce en una generalización efectiva, como lo demuestra una precisión de 0% en el conjunto de validación. La alta pérdida en el conjunto de validación confirma la falta de generalización del modelo.
Aunque la detención temprana del entrenamiento en la undécima época indica un intento de mitigar el sobreajuste, los resultados sugieren la necesidad de abordar más eficazmente este problema mediante estrategias adicionales, como la regularización, para mejorar la capacidad de generalización del modelo. Esto significa que para mejorar las métricas es necesario buscar hiperparámetros que ayuden a simplificar la red neuronal o que cambien el comportamiento y método de decisión de la red neuronal.
model_accuracy = model.evaluate(X_test_p, Y_test)
print("Model Accuracy:", model_accuracy)
63/63 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.3133 - loss: 2.7989 Model Accuracy: [3.4156370162963867, 0.3034999966621399]
def plot_confusion_matrix(y_true, y_pred, classes,
normalize=False,
title=None,
cmap=plt.cm.Blues,size=(10,10)):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if not title:
if normalize:
title = 'Normalized confusion matrix'
else:
title = 'Confusion matrix, without normalization'
# Compute confusion matrix
cm = confusion_matrix(y_true, y_pred)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
fig, ax = plt.subplots(figsize=size)
im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
# We want to show all ticks...
ax.set(xticks=np.arange(cm.shape[1]),
yticks=np.arange(cm.shape[0]),
# ... and label them with the respective list entries
xticklabels=classes, yticklabels=classes,
title=title,
ylabel='True label',
xlabel='Predicted label')
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
# Loop over data dimensions and create text annotations.
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt),
ha="center", va="center",
color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()
return ax
Nuevamente es posible evidenciar el sobreajuste del modelo y la incapacidad de clasificar los datos como de tipo technology.
Ahora que se cuenta con el algoritmo base de la red neuronal, el enfoque se dirige hacia la optimización de su rendimiento a través de una búsqueda de hiperparámetros. Se ha observado que la red neuronal puede no generalizar adecuadamente para datos desconocidos, lo que sugiere que hay margen para mejorar su capacidad de clasificación y sobre todo reducir su complejidad. En esta fase de optimización, se explorará el tipo de optimizador a utilizar, la cantidad de neuronas en la capa oculta y los métodos de activación de la capa de entrada y oculta. La elección del optimizador es crucial ya que determina cómo se actualizan los pesos de la red durante el entrenamiento, lo que puede influir significativamente en la convergencia y la calidad de los resultados. Del mismo modo, la cantidad de neuronas en la capa oculta influye en la capacidad de la red para aprender representaciones más complejas y no lineales de los datos; como se vió a través de las gráficas de validación y de entrenamiento puede que sea recomendable reducir el número de neuronas presente en la capa oculta.
from scikeras.wrappers import KerasClassifier
from sklearn.model_selection import GridSearchCV
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
# Define la función para crear tu modelo
def create_model_5100(optimizer='adam', units=128, activation='relu'):
model = Sequential(name="Hyp_NN_5100")
model.add(Dense(128, activation=activation, input_shape=(X_train_p_5100.shape[1],), name="Input_Layer"))
model.add(Dense(units=units, activation=activation, name="Hidden_Layer"))
model.add(Dense(len(unique_labels), activation='softmax', name='Output_Layer'))
model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
return model
def create_model_3000(optimizer='adam', units=128, activation='relu'):
model = Sequential(name="Hyp_NN_3000")
model.add(Dense(128, activation=activation, input_shape=(X_train_p_3000.shape[1],), name="Input_Layer"))
model.add(Dense(units=units, activation=activation, name="Hidden_Layer"))
model.add(Dense(len(unique_labels), activation='softmax', name='Output_Layer'))
model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
return model
def create_model_1700(optimizer='adam', units=128, activation='relu'):
model = Sequential(name="Hyp_NN_1700")
model.add(Dense(128, activation=activation, input_shape=(X_train_p_1700.shape[1],), name="Input_Layer"))
model.add(Dense(units=units, activation=activation, name="Hidden_Layer"))
model.add(Dense(len(unique_labels), activation='softmax', name='Output_Layer'))
model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
return model
# Crea el clasificador Keras para utilizarlo con GridSearchCV
keras_classifier_5100 = KerasClassifier(build_fn=create_model_5100, batch_size=20, verbose=2)
keras_classifier_3000 = KerasClassifier(build_fn=create_model_3000, batch_size=20, verbose=2)
keras_classifier_1700 = KerasClassifier(build_fn=create_model_1700, batch_size=20, verbose=2)
# Define los hiperparámetros que deseas buscar
param_dist = {
'optimizer': ['adam', 'rmsprop', 'sgd', 'adagrad'],
'model__activation': ['relu', 'sigmoid'], # Cambié 'activation' de 'act'
'model__units': [16, 32, 64, 128]
}
# Realiza la búsqueda de hiperparámetros utilizando GridSearchCV
grid_search = GridSearchCV(estimator=keras_classifier_5100, param_grid=param_dist, cv=3, verbose=2)
grid_result = grid_search.fit(X_train_p_5100, Y_train)
Fitting 3 folds for each of 32 candidates, totalling 96 fits
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.7437 - loss: 0.9738 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=16, optimizer=adam; total time= 1.4s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 4ms/step - accuracy: 0.7114 - loss: 0.9425 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=16, optimizer=adam; total time= 1.4s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 4ms/step - accuracy: 0.8420 - loss: 0.8968 134/134 - 0s - 990us/step [CV] END model__activation=relu, model__units=16, optimizer=adam; total time= 1.4s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 4ms/step - accuracy: 0.7812 - loss: 0.9056 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=16, optimizer=rmsprop; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 4ms/step - accuracy: 0.8011 - loss: 0.8975 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=16, optimizer=rmsprop; total time= 1.4s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 4ms/step - accuracy: 0.7540 - loss: 0.9172 134/134 - 0s - 996us/step [CV] END model__activation=relu, model__units=16, optimizer=rmsprop; total time= 1.4s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 4ms/step - accuracy: 0.7772 - loss: 0.8779 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=16, optimizer=sgd; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8200 - loss: 0.8916 134/134 - 0s - 979us/step [CV] END model__activation=relu, model__units=16, optimizer=sgd; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8384 - loss: 0.8860 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=16, optimizer=sgd; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.7780 - loss: 0.8713 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=16, optimizer=adagrad; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.7620 - loss: 0.9072 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=16, optimizer=adagrad; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 4ms/step - accuracy: 0.8241 - loss: 0.8880 134/134 - 0s - 975us/step [CV] END model__activation=relu, model__units=16, optimizer=adagrad; total time= 1.4s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 4ms/step - accuracy: 0.8279 - loss: 0.8083 134/134 - 0s - 994us/step [CV] END model__activation=relu, model__units=32, optimizer=adam; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8312 - loss: 0.7948 134/134 - 0s - 971us/step [CV] END model__activation=relu, model__units=32, optimizer=adam; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8309 - loss: 0.8142 134/134 - 0s - 964us/step [CV] END model__activation=relu, model__units=32, optimizer=adam; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8230 - loss: 0.8129 134/134 - 0s - 952us/step [CV] END model__activation=relu, model__units=32, optimizer=rmsprop; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8564 - loss: 0.7785 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=32, optimizer=rmsprop; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8618 - loss: 0.7929 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=32, optimizer=rmsprop; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8194 - loss: 0.8112 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=32, optimizer=sgd; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8391 - loss: 0.8063 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=32, optimizer=sgd; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8101 - loss: 0.8012 134/134 - 0s - 979us/step [CV] END model__activation=relu, model__units=32, optimizer=sgd; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8131 - loss: 0.8255 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=32, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8738 - loss: 0.7743 134/134 - 0s - 993us/step [CV] END model__activation=relu, model__units=32, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8200 - loss: 0.8144 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=32, optimizer=adagrad; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8207 - loss: 0.7119 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=64, optimizer=adam; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8528 - loss: 0.7033 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=64, optimizer=adam; total time= 1.4s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8472 - loss: 0.7192 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=64, optimizer=adam; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8209 - loss: 0.7257 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=64, optimizer=rmsprop; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 4ms/step - accuracy: 0.8217 - loss: 0.6985 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=64, optimizer=rmsprop; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8333 - loss: 0.7189 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=64, optimizer=rmsprop; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8584 - loss: 0.7163 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=64, optimizer=sgd; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8472 - loss: 0.6999 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=64, optimizer=sgd; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8320 - loss: 0.7133 134/134 - 0s - 986us/step [CV] END model__activation=relu, model__units=64, optimizer=sgd; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8586 - loss: 0.7244 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=64, optimizer=adagrad; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8562 - loss: 0.7011 134/134 - 0s - 982us/step [CV] END model__activation=relu, model__units=64, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8510 - loss: 0.7158 134/134 - 0s - 960us/step [CV] END model__activation=relu, model__units=64, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8213 - loss: 0.6654 134/134 - 0s - 964us/step [CV] END model__activation=relu, model__units=128, optimizer=adam; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8247 - loss: 0.6459 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=128, optimizer=adam; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8615 - loss: 0.6482 134/134 - 0s - 971us/step [CV] END model__activation=relu, model__units=128, optimizer=adam; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8425 - loss: 0.6385 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=128, optimizer=rmsprop; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8534 - loss: 0.6353 134/134 - 0s - 990us/step [CV] END model__activation=relu, model__units=128, optimizer=rmsprop; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8421 - loss: 0.6451 134/134 - 0s - 975us/step [CV] END model__activation=relu, model__units=128, optimizer=rmsprop; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8582 - loss: 0.6305 134/134 - 0s - 967us/step [CV] END model__activation=relu, model__units=128, optimizer=sgd; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8871 - loss: 0.6285 134/134 - 0s - 941us/step [CV] END model__activation=relu, model__units=128, optimizer=sgd; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8553 - loss: 0.6513 134/134 - 0s - 945us/step [CV] END model__activation=relu, model__units=128, optimizer=sgd; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8245 - loss: 0.6388 134/134 - 0s - 979us/step [CV] END model__activation=relu, model__units=128, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8412 - loss: 0.6436 134/134 - 0s - 960us/step [CV] END model__activation=relu, model__units=128, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8183 - loss: 0.6763 134/134 - 0s - 967us/step [CV] END model__activation=relu, model__units=128, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2306 - loss: 1.6097 134/134 - 0s - 949us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=adam; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.3160 - loss: 1.5971 134/134 - 0s - 960us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=adam; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2422 - loss: 1.5985 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=16, optimizer=adam; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2704 - loss: 1.6007 134/134 - 0s - 961us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=rmsprop; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2605 - loss: 1.6035 134/134 - 0s - 956us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=rmsprop; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2683 - loss: 1.5992 134/134 - 0s - 979us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=rmsprop; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2425 - loss: 1.6014 134/134 - 0s - 961us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=sgd; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2368 - loss: 1.6026 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=16, optimizer=sgd; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2647 - loss: 1.5964 134/134 - 0s - 966us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=sgd; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2704 - loss: 1.5998 134/134 - 0s - 979us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2599 - loss: 1.5978 134/134 - 0s - 949us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2338 - loss: 1.6097 134/134 - 0s - 967us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2428 - loss: 1.6048 134/134 - 0s - 967us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=adam; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2689 - loss: 1.5997 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=32, optimizer=adam; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 4ms/step - accuracy: 0.2525 - loss: 1.5950 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=32, optimizer=adam; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2333 - loss: 1.6021 134/134 - 0s - 960us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=rmsprop; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2365 - loss: 1.6041 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=32, optimizer=rmsprop; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2720 - loss: 1.5987 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=32, optimizer=rmsprop; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2646 - loss: 1.6008 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=32, optimizer=sgd; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2475 - loss: 1.6024 134/134 - 0s - 993us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=sgd; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2441 - loss: 1.6082 134/134 - 0s - 941us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=sgd; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2297 - loss: 1.6066 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=32, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2402 - loss: 1.6078 134/134 - 0s - 967us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2368 - loss: 1.6138 134/134 - 0s - 945us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2398 - loss: 1.6052 134/134 - 0s - 994us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=adam; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 3s - 13ms/step - accuracy: 0.2631 - loss: 1.6046 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=64, optimizer=adam; total time= 3.9s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2327 - loss: 1.6021 134/134 - 0s - 956us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=adam; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2265 - loss: 1.6029 134/134 - 0s - 956us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=rmsprop; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2303 - loss: 1.6081 134/134 - 0s - 968us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=rmsprop; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2417 - loss: 1.6020 134/134 - 0s - 979us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=rmsprop; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2321 - loss: 1.6054 134/134 - 0s - 975us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=sgd; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2216 - loss: 1.6082 134/134 - 0s - 956us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=sgd; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2351 - loss: 1.6068 134/134 - 0s - 979us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=sgd; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2235 - loss: 1.6043 134/134 - 0s - 960us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2235 - loss: 1.6119 134/134 - 0s - 968us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2263 - loss: 1.6059 134/134 - 0s - 976us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2196 - loss: 1.6104 134/134 - 0s - 982us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=adam; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2192 - loss: 1.6170 134/134 - 0s - 979us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=adam; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2278 - loss: 1.6167 134/134 - 0s - 994us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=adam; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2267 - loss: 1.6118 134/134 - 0s - 980us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=rmsprop; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2353 - loss: 1.6064 134/134 - 0s - 963us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=rmsprop; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2237 - loss: 1.6126 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=128, optimizer=rmsprop; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2130 - loss: 1.6129 134/134 - 0s - 975us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=sgd; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2291 - loss: 1.6129 134/134 - 0s - 989us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=sgd; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2285 - loss: 1.6100 134/134 - 0s - 965us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=sgd; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2295 - loss: 1.6084 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=128, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2342 - loss: 1.6082 134/134 - 0s - 975us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2308 - loss: 1.6088 134/134 - 0s - 964us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
400/400 - 1s - 3ms/step - accuracy: 0.8854 - loss: 0.5120
A continuación se mostrarán los hiperparámetros seleccionados a través del GridSearch:
# Muestra los resultados
print("Mejor precisión obtenida: {:.2f}%".format(grid_result.best_score_ * 100))
print("Mejores hiperparámetros encontrados:", grid_result.best_params_)
Mejor precisión obtenida: 97.87%
Mejores hiperparámetros encontrados: {'model__activation': 'relu', 'model__units': 64, 'optimizer': 'adam'}
# Realiza la búsqueda de hiperparámetros utilizando GridSearchCV
grid_search_2 = GridSearchCV(estimator=keras_classifier_3000, param_grid=param_dist, cv=3, verbose=2)
grid_result_2 = grid_search_2.fit(X_train_p_3000, Y_train)
Fitting 3 folds for each of 32 candidates, totalling 96 fits
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.7911 - loss: 0.8885 134/134 - 0s - 951us/step [CV] END model__activation=relu, model__units=16, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.6550 - loss: 0.9532 134/134 - 0s - 990us/step [CV] END model__activation=relu, model__units=16, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.7990 - loss: 0.9420 134/134 - 0s - 934us/step [CV] END model__activation=relu, model__units=16, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.7600 - loss: 0.8807 134/134 - 0s - 994us/step [CV] END model__activation=relu, model__units=16, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.7930 - loss: 0.8949 134/134 - 0s - 915us/step [CV] END model__activation=relu, model__units=16, optimizer=rmsprop; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8114 - loss: 0.9253 134/134 - 0s - 920us/step [CV] END model__activation=relu, model__units=16, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.7915 - loss: 0.8929 134/134 - 0s - 906us/step [CV] END model__activation=relu, model__units=16, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.7819 - loss: 0.8826 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=16, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.7769 - loss: 0.8986 134/134 - 0s - 942us/step [CV] END model__activation=relu, model__units=16, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8329 - loss: 0.8857 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=16, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8048 - loss: 0.9064 134/134 - 0s - 973us/step [CV] END model__activation=relu, model__units=16, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8030 - loss: 0.9340 134/134 - 0s - 937us/step [CV] END model__activation=relu, model__units=16, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8247 - loss: 0.8152 134/134 - 0s - 967us/step [CV] END model__activation=relu, model__units=32, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8322 - loss: 0.7917 134/134 - 0s - 952us/step [CV] END model__activation=relu, model__units=32, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8354 - loss: 0.8113 134/134 - 0s - 956us/step [CV] END model__activation=relu, model__units=32, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8333 - loss: 0.8072 134/134 - 0s - 964us/step [CV] END model__activation=relu, model__units=32, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8543 - loss: 0.8212 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=32, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8045 - loss: 0.8119 134/134 - 0s - 964us/step [CV] END model__activation=relu, model__units=32, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8442 - loss: 0.7902 134/134 - 0s - 915us/step [CV] END model__activation=relu, model__units=32, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8359 - loss: 0.8201 134/134 - 0s - 972us/step [CV] END model__activation=relu, model__units=32, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8427 - loss: 0.7934 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=32, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8251 - loss: 0.7976 134/134 - 0s - 928us/step [CV] END model__activation=relu, model__units=32, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8311 - loss: 0.8074 134/134 - 0s - 933us/step [CV] END model__activation=relu, model__units=32, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8337 - loss: 0.7967 134/134 - 0s - 953us/step [CV] END model__activation=relu, model__units=32, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8530 - loss: 0.6996 134/134 - 0s - 964us/step [CV] END model__activation=relu, model__units=64, optimizer=adam; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8654 - loss: 0.7099 134/134 - 0s - 934us/step [CV] END model__activation=relu, model__units=64, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8528 - loss: 0.7292 134/134 - 0s - 960us/step [CV] END model__activation=relu, model__units=64, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8474 - loss: 0.7117 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=64, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8297 - loss: 0.7142 134/134 - 0s - 934us/step [CV] END model__activation=relu, model__units=64, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8556 - loss: 0.7120 134/134 - 0s - 911us/step [CV] END model__activation=relu, model__units=64, optimizer=rmsprop; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8297 - loss: 0.7316 134/134 - 0s - 930us/step [CV] END model__activation=relu, model__units=64, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8425 - loss: 0.7150 134/134 - 0s - 943us/step [CV] END model__activation=relu, model__units=64, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8502 - loss: 0.7229 134/134 - 0s - 908us/step [CV] END model__activation=relu, model__units=64, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8296 - loss: 0.7228 134/134 - 0s - 967us/step [CV] END model__activation=relu, model__units=64, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8577 - loss: 0.7077 134/134 - 0s - 964us/step [CV] END model__activation=relu, model__units=64, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8682 - loss: 0.7153 134/134 - 0s - 952us/step [CV] END model__activation=relu, model__units=64, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8438 - loss: 0.6651 134/134 - 0s - 947us/step [CV] END model__activation=relu, model__units=128, optimizer=adam; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8378 - loss: 0.6536 134/134 - 0s - 979us/step [CV] END model__activation=relu, model__units=128, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8540 - loss: 0.6467 134/134 - 0s - 977us/step [CV] END model__activation=relu, model__units=128, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8372 - loss: 0.6453 134/134 - 0s - 960us/step [CV] END model__activation=relu, model__units=128, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8256 - loss: 0.6441 134/134 - 0s - 967us/step [CV] END model__activation=relu, model__units=128, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8585 - loss: 0.6333 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=128, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8678 - loss: 0.6260 134/134 - 0s - 911us/step [CV] END model__activation=relu, model__units=128, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8556 - loss: 0.6479 134/134 - 0s - 937us/step [CV] END model__activation=relu, model__units=128, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8358 - loss: 0.6571 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=128, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8408 - loss: 0.6395 134/134 - 0s - 952us/step [CV] END model__activation=relu, model__units=128, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8281 - loss: 0.6461 134/134 - 0s - 943us/step [CV] END model__activation=relu, model__units=128, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8553 - loss: 0.6431 134/134 - 0s - 945us/step [CV] END model__activation=relu, model__units=128, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2507 - loss: 1.6080 134/134 - 0s - 937us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2357 - loss: 1.6303 134/134 - 0s - 904us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2613 - loss: 1.6013 134/134 - 0s - 960us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2340 - loss: 1.6161 134/134 - 0s - 987us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2500 - loss: 1.5992 134/134 - 0s - 934us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2501 - loss: 1.5961 134/134 - 0s - 954us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2419 - loss: 1.6066 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=16, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2578 - loss: 1.6017 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=16, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2685 - loss: 1.6031 134/134 - 0s - 971us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2816 - loss: 1.6037 134/134 - 0s - 956us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2612 - loss: 1.5995 134/134 - 0s - 979us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2497 - loss: 1.5958 134/134 - 0s - 915us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2466 - loss: 1.5992 134/134 - 0s - 922us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2443 - loss: 1.5974 134/134 - 0s - 923us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2478 - loss: 1.5978 134/134 - 0s - 997us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2723 - loss: 1.5994 134/134 - 0s - 972us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 4s - 16ms/step - accuracy: 0.2528 - loss: 1.6034 134/134 - 0s - 963us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=rmsprop; total time= 4.5s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2366 - loss: 1.5988 134/134 - 0s - 930us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2464 - loss: 1.6040 134/134 - 0s - 986us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2498 - loss: 1.5977 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=32, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2377 - loss: 1.5972 134/134 - 0s - 971us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2548 - loss: 1.5986 134/134 - 0s - 934us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2501 - loss: 1.5963 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=32, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2383 - loss: 1.5982 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=32, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2196 - loss: 1.6023 134/134 - 0s - 916us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2250 - loss: 1.6027 134/134 - 0s - 961us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2467 - loss: 1.6003 134/134 - 0s - 979us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2224 - loss: 1.6061 134/134 - 0s - 952us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2440 - loss: 1.6027 134/134 - 0s - 997us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2447 - loss: 1.6003 134/134 - 0s - 945us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2366 - loss: 1.6009 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=64, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2365 - loss: 1.6064 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=64, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2357 - loss: 1.6032 134/134 - 0s - 975us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2248 - loss: 1.6159 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=64, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2305 - loss: 1.6074 134/134 - 0s - 978us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=adagrad; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2293 - loss: 1.6065 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=64, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2314 - loss: 1.6158 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=128, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2327 - loss: 1.6076 134/134 - 0s - 989us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2227 - loss: 1.6092 134/134 - 0s - 942us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2271 - loss: 1.6084 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=128, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2310 - loss: 1.6065 134/134 - 0s - 967us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2297 - loss: 1.6052 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=128, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2226 - loss: 1.6093 134/134 - 0s - 960us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2188 - loss: 1.6124 134/134 - 0s - 926us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2484 - loss: 1.6040 134/134 - 0s - 923us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2340 - loss: 1.6127 134/134 - 0s - 905us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2310 - loss: 1.6105 134/134 - 0s - 919us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2297 - loss: 1.6100 134/134 - 0s - 956us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
400/400 - 1s - 2ms/step - accuracy: 0.8710 - loss: 0.5746
print("Mejor precisión obtenida: {:.2f}%".format(grid_result_2.best_score_ * 100))
print("Mejores hiperparámetros encontrados:", grid_result_2.best_params_)
Mejor precisión obtenida: 97.89%
Mejores hiperparámetros encontrados: {'model__activation': 'relu', 'model__units': 32, 'optimizer': 'adam'}
# Realiza la búsqueda de hiperparámetros utilizando GridSearchCV
grid_search_3 = GridSearchCV(estimator=keras_classifier_1700, param_grid=param_dist, cv=3, verbose=2)
grid_result_3 = grid_search_3.fit(X_train_p_1700, Y_train)
Fitting 3 folds for each of 32 candidates, totalling 96 fits
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8462 - loss: 0.8574 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=16, optimizer=adam; total time= 1.5s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8018 - loss: 0.8717 134/134 - 0s - 964us/step [CV] END model__activation=relu, model__units=16, optimizer=adam; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8063 - loss: 0.8687 134/134 - 0s - 949us/step [CV] END model__activation=relu, model__units=16, optimizer=adam; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.7986 - loss: 0.9384 134/134 - 0s - 926us/step [CV] END model__activation=relu, model__units=16, optimizer=rmsprop; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.7718 - loss: 0.9476 134/134 - 0s - 986us/step [CV] END model__activation=relu, model__units=16, optimizer=rmsprop; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8131 - loss: 0.8810 134/134 - 0s - 986us/step [CV] END model__activation=relu, model__units=16, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8164 - loss: 0.8880 134/134 - 0s - 925us/step [CV] END model__activation=relu, model__units=16, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.7362 - loss: 0.9230 134/134 - 0s - 945us/step [CV] END model__activation=relu, model__units=16, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8046 - loss: 0.9038 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=16, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.7544 - loss: 0.9571 134/134 - 0s - 914us/step [CV] END model__activation=relu, model__units=16, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.7905 - loss: 0.9515 134/134 - 0s - 934us/step [CV] END model__activation=relu, model__units=16, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8170 - loss: 0.8948 134/134 - 0s - 915us/step [CV] END model__activation=relu, model__units=16, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.7934 - loss: 0.8330 134/134 - 0s - 953us/step [CV] END model__activation=relu, model__units=32, optimizer=adam; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8266 - loss: 0.7867 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=32, optimizer=adam; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8268 - loss: 0.8048 134/134 - 0s - 949us/step [CV] END model__activation=relu, model__units=32, optimizer=adam; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.7952 - loss: 0.8125 134/134 - 0s - 950us/step [CV] END model__activation=relu, model__units=32, optimizer=rmsprop; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8159 - loss: 0.8189 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=32, optimizer=rmsprop; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8166 - loss: 0.8258 134/134 - 0s - 941us/step [CV] END model__activation=relu, model__units=32, optimizer=rmsprop; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8020 - loss: 0.8059 134/134 - 0s - 924us/step [CV] END model__activation=relu, model__units=32, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8236 - loss: 0.8109 134/134 - 0s - 915us/step [CV] END model__activation=relu, model__units=32, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8459 - loss: 0.8065 134/134 - 0s - 915us/step [CV] END model__activation=relu, model__units=32, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8464 - loss: 0.7953 134/134 - 0s - 930us/step [CV] END model__activation=relu, model__units=32, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8234 - loss: 0.8132 134/134 - 0s - 967us/step [CV] END model__activation=relu, model__units=32, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.7810 - loss: 0.8184 134/134 - 0s - 926us/step [CV] END model__activation=relu, model__units=32, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8517 - loss: 0.7191 134/134 - 0s - 941us/step [CV] END model__activation=relu, model__units=64, optimizer=adam; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8449 - loss: 0.6989 134/134 - 0s - 911us/step [CV] END model__activation=relu, model__units=64, optimizer=adam; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8577 - loss: 0.7071 134/134 - 0s - 908us/step [CV] END model__activation=relu, model__units=64, optimizer=adam; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8461 - loss: 0.7059 134/134 - 0s - 952us/step [CV] END model__activation=relu, model__units=64, optimizer=rmsprop; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8633 - loss: 0.6990 134/134 - 0s - 971us/step [CV] END model__activation=relu, model__units=64, optimizer=rmsprop; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8315 - loss: 0.7187 134/134 - 0s - 912us/step [CV] END model__activation=relu, model__units=64, optimizer=rmsprop; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8631 - loss: 0.7182 134/134 - 0s - 894us/step [CV] END model__activation=relu, model__units=64, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8378 - loss: 0.7171 134/134 - 0s - 934us/step [CV] END model__activation=relu, model__units=64, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8403 - loss: 0.7152 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=64, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8524 - loss: 0.6979 134/134 - 0s - 938us/step [CV] END model__activation=relu, model__units=64, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8652 - loss: 0.6842 134/134 - 0s - 937us/step [CV] END model__activation=relu, model__units=64, optimizer=adagrad; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8526 - loss: 0.7150 134/134 - 0s - 919us/step [CV] END model__activation=relu, model__units=64, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8387 - loss: 0.6430 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=128, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 4ms/step - accuracy: 0.8646 - loss: 0.6371 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=128, optimizer=adam; total time= 1.2s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8579 - loss: 0.6482 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=128, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8547 - loss: 0.6383 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=128, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8509 - loss: 0.6341 134/134 - 0s - 1ms/step [CV] END model__activation=relu, model__units=128, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8448 - loss: 0.6395 134/134 - 0s - 956us/step [CV] END model__activation=relu, model__units=128, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8504 - loss: 0.6405 134/134 - 0s - 977us/step [CV] END model__activation=relu, model__units=128, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8652 - loss: 0.6270 134/134 - 0s - 945us/step [CV] END model__activation=relu, model__units=128, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8487 - loss: 0.6417 134/134 - 0s - 937us/step [CV] END model__activation=relu, model__units=128, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8498 - loss: 0.6211 134/134 - 0s - 966us/step [CV] END model__activation=relu, model__units=128, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8442 - loss: 0.6386 134/134 - 0s - 923us/step [CV] END model__activation=relu, model__units=128, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.8596 - loss: 0.6418 134/134 - 0s - 924us/step [CV] END model__activation=relu, model__units=128, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2661 - loss: 1.5957 134/134 - 0s - 907us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=adam; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2486 - loss: 1.5973 134/134 - 0s - 990us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=adam; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2730 - loss: 1.5967 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=16, optimizer=adam; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2365 - loss: 1.5987 134/134 - 0s - 936us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=rmsprop; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2526 - loss: 1.6061 134/134 - 0s - 935us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=rmsprop; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2615 - loss: 1.5974 134/134 - 0s - 991us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=rmsprop; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2550 - loss: 1.5915 134/134 - 0s - 934us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2732 - loss: 1.6057 134/134 - 0s - 927us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2885 - loss: 1.5996 134/134 - 0s - 927us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2533 - loss: 1.6051 134/134 - 0s - 908us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 4ms/step - accuracy: 0.2402 - loss: 1.5978 134/134 - 0s - 910us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=adagrad; total time= 1.3s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2456 - loss: 1.6157 134/134 - 0s - 889us/step [CV] END model__activation=sigmoid, model__units=16, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2361 - loss: 1.5954 134/134 - 0s - 919us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=adam; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2501 - loss: 1.6312 134/134 - 0s - 905us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=adam; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2546 - loss: 1.5988 134/134 - 0s - 958us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=adam; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2539 - loss: 1.5974 134/134 - 0s - 960us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=rmsprop; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2376 - loss: 1.5991 134/134 - 0s - 956us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=rmsprop; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2703 - loss: 1.5998 134/134 - 0s - 906us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=rmsprop; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2511 - loss: 1.5937 134/134 - 0s - 889us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2265 - loss: 1.6046 134/134 - 0s - 885us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2463 - loss: 1.5992 134/134 - 0s - 890us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2545 - loss: 1.6008 134/134 - 0s - 908us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 5s - 17ms/step - accuracy: 0.2485 - loss: 1.5936 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=32, optimizer=adagrad; total time= 4.8s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2690 - loss: 1.5967 134/134 - 0s - 904us/step [CV] END model__activation=sigmoid, model__units=32, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2301 - loss: 1.6019 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=64, optimizer=adam; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2344 - loss: 1.5991 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=64, optimizer=adam; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2398 - loss: 1.6010 134/134 - 0s - 987us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=adam; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2507 - loss: 1.6045 134/134 - 0s - 964us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2395 - loss: 1.6013 134/134 - 0s - 945us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=rmsprop; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2447 - loss: 1.6001 134/134 - 0s - 980us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=rmsprop; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2391 - loss: 1.5975 134/134 - 0s - 984us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2443 - loss: 1.5994 134/134 - 0s - 967us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2171 - loss: 1.6022 134/134 - 0s - 950us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2417 - loss: 1.6035 134/134 - 0s - 971us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2395 - loss: 1.6067 134/134 - 0s - 975us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2214 - loss: 1.6075 134/134 - 0s - 931us/step [CV] END model__activation=sigmoid, model__units=64, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2293 - loss: 1.6092 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=128, optimizer=adam; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2445 - loss: 1.6047 134/134 - 0s - 997us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=adam; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2313 - loss: 1.6073 134/134 - 0s - 941us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=adam; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2170 - loss: 1.6082 134/134 - 0s - 982us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=rmsprop; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2245 - loss: 1.6080 134/134 - 0s - 986us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=rmsprop; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2263 - loss: 1.6067 134/134 - 0s - 960us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=rmsprop; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2252 - loss: 1.6050 134/134 - 0s - 996us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2200 - loss: 1.6129 134/134 - 0s - 1ms/step [CV] END model__activation=sigmoid, model__units=128, optimizer=sgd; total time= 1.1s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2252 - loss: 1.6093 134/134 - 0s - 967us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=sgd; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2297 - loss: 1.6114 134/134 - 0s - 982us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2207 - loss: 1.6086 134/134 - 0s - 966us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
267/267 - 1s - 3ms/step - accuracy: 0.2259 - loss: 1.6057 134/134 - 0s - 934us/step [CV] END model__activation=sigmoid, model__units=128, optimizer=adagrad; total time= 1.0s
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\scikeras\wrappers.py:925: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead. X, y = self._initialize(X, y) c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
400/400 - 1s - 2ms/step - accuracy: 0.8587 - loss: 0.6449
print("Mejor precisión obtenida: {:.2f}%".format(grid_result_3.best_score_ * 100))
print("Mejores hiperparámetros encontrados:", grid_result_3.best_params_)
Mejor precisión obtenida: 97.91%
Mejores hiperparámetros encontrados: {'model__activation': 'relu', 'model__units': 16, 'optimizer': 'adam'}
Ahora se revisarán nuevamente las métricas para el conjunto test.
# Get the best parameters
best_params_5100 = grid_result.best_params_
# Create a new model using the best parameters
best_model_5100 = create_model_5100(optimizer=best_params_5100['optimizer'], units=best_params_5100['model__units'], activation=best_params_5100['model__activation'])
# Train the best model on the entire training dataset
best_model_5100.fit(X_train_p_5100, Y_train, batch_size=20, epochs=50, verbose=1)
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
Epoch 1/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.7477 - loss: 1.0147 Epoch 2/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9981 - loss: 0.0100 Epoch 3/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9992 - loss: 0.0049 Epoch 4/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9998 - loss: 0.0028 Epoch 5/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 5.9603e-04 Epoch 6/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 3.3352e-04 Epoch 7/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 2.2002e-04 Epoch 8/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 1.5910e-04 Epoch 9/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 1.1885e-04 Epoch 10/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 8.5320e-05 Epoch 11/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 6.3026e-05 Epoch 12/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 4.5463e-05 Epoch 13/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 3.1682e-05 Epoch 14/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 1.0000 - loss: 1.9581e-05 Epoch 15/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 1.2378e-05 Epoch 16/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 8.1555e-06 Epoch 17/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 5.9651e-06 Epoch 18/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 4.4351e-06 Epoch 19/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 3.4079e-06 Epoch 20/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 2.5136e-06 Epoch 21/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 1.0000 - loss: 2.0880e-06 Epoch 22/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 1.6156e-06 Epoch 23/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 1.2751e-06 Epoch 24/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 1.0072e-06 Epoch 25/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 8.3258e-07 Epoch 26/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 6.4831e-07 Epoch 27/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 5.1146e-07 Epoch 28/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 4.1528e-07 Epoch 29/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 3.3615e-07 Epoch 30/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 2.8169e-07 Epoch 31/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 2.2631e-07 Epoch 32/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 1.7601e-07 Epoch 33/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 1.4074e-07 Epoch 34/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 1.1910e-07 Epoch 35/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 9.7406e-08 Epoch 36/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 8.0025e-08 Epoch 37/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 6.5909e-08 Epoch 38/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - accuracy: 1.0000 - loss: 5.0849e-08 Epoch 39/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 4.1691e-08 Epoch 40/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 3.6849e-08 Epoch 41/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 2.9797e-08 Epoch 42/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 2.3438e-08 Epoch 43/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 1.9923e-08 Epoch 44/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 1.7495e-08 Epoch 45/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 1.3067e-08 Epoch 46/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 1.0347e-08 Epoch 47/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 7.4338e-09 Epoch 48/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 5.6877e-09 Epoch 49/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 4.3448e-09 Epoch 50/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 1.0000 - loss: 3.3934e-09
<keras.src.callbacks.history.History at 0x29a2dcf8890>
model_accuracy_t_5100 = best_model_5100.evaluate(X_test_p_5100, Y_test)
print("Model Accuracy:", model_accuracy_t_5100)
63/63 ━━━━━━━━━━━━━━━━━━━━ 0s 906us/step - accuracy: 0.9660 - loss: 0.2049 Model Accuracy: [0.15234297513961792, 0.9725000262260437]
Y_pred_hyp = best_model_5100.predict(X_test_p_5100)
# Encuentra el índice del valor máximo en cada fila
clases_predichas = np.argmax(Y_pred_hyp, axis=1)
print(clases_predichas)
print(classification_report(Y_test, clases_predichas))
plot_confusion_matrix(y_true=Y_test, y_pred=clases_predichas, classes=unique_labels, normalize=False,
title='Matriz de Confusión')
1/63 ━━━━━━━━━━━━━━━━━━━━ 3s 52ms/step63/63 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step [0 0 0 ... 4 4 4] precision recall f1-score support 0 0.96 0.95 0.95 400 1 0.99 0.98 0.98 400 2 0.98 0.99 0.99 400 3 0.99 0.98 0.99 400 4 0.95 0.96 0.95 400 accuracy 0.97 2000 macro avg 0.97 0.97 0.97 2000 weighted avg 0.97 0.97 0.97 2000 Confusion matrix, without normalization
<Axes: title={'center': 'Matriz de Confusión'}, xlabel='Predicted label', ylabel='True label'>
# Get the best parameters
best_params_3000 = grid_result_2.best_params_
# Create a new model using the best parameters
best_model_3000 = create_model_3000(optimizer=best_params_3000['optimizer'], units=best_params_3000['model__units'], activation=best_params_3000['model__activation'])
# Train the best model on the entire training dataset
best_model_3000.fit(X_train_p_3000, Y_train, batch_size=20, epochs=50, verbose=1)
Epoch 1/50
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.6882 - loss: 1.0815 Epoch 2/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.9952 - loss: 0.0249 Epoch 3/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.9986 - loss: 0.0069 Epoch 4/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.9996 - loss: 0.0026 Epoch 5/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.9999 - loss: 0.0012 Epoch 6/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 1.0000 - loss: 5.9947e-04 Epoch 7/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 1.0000 - loss: 3.6952e-04 Epoch 8/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 2.5054e-04 Epoch 9/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 1.8275e-04 Epoch 10/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 1.4103e-04 Epoch 11/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 1.0637e-04 Epoch 12/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 7.7230e-05 Epoch 13/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 6.3224e-05 Epoch 14/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 4.5620e-05 Epoch 15/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 3.6953e-05 Epoch 16/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 2.9588e-05 Epoch 17/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 2.2522e-05 Epoch 18/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 1.7647e-05 Epoch 19/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 1.0000 - loss: 1.3656e-05 Epoch 20/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 1.1410e-05 Epoch 21/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 9.1350e-06 Epoch 22/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 7.2834e-06 Epoch 23/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 5.6306e-06 Epoch 24/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 4.5560e-06 Epoch 25/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 3.5398e-06 Epoch 26/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 2.8462e-06 Epoch 27/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 1.0000 - loss: 2.2551e-06 Epoch 28/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 1.7837e-06 Epoch 29/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 1.4562e-06 Epoch 30/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 1.1107e-06 Epoch 31/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 9.5766e-07 Epoch 32/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 1.0000 - loss: 7.4438e-07 Epoch 33/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 6.1263e-07 Epoch 34/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 1.0000 - loss: 4.8089e-07 Epoch 35/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 1.0000 - loss: 3.8908e-07 Epoch 36/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 3.0514e-07 Epoch 37/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 2.4797e-07 Epoch 38/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 1.9950e-07 Epoch 39/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 1.5905e-07 Epoch 40/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 1.0000 - loss: 1.2901e-07 Epoch 41/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 1.0855e-07 Epoch 42/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 1.0000 - loss: 9.0086e-08 Epoch 43/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 1.0000 - loss: 7.2976e-08 Epoch 44/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 5.8750e-08 Epoch 45/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 4.6693e-08 Epoch 46/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 3.7244e-08 Epoch 47/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 1.0000 - loss: 2.9592e-08 Epoch 48/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 2.5957e-08 Epoch 49/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 2.0900e-08 Epoch 50/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 1.6459e-08
<keras.src.callbacks.history.History at 0x29a2e37b610>
model_accuracy_t_3000 = best_model_3000.evaluate(X_test_p_3000, Y_test)
print("Model Accuracy:", model_accuracy_t_3000)
63/63 ━━━━━━━━━━━━━━━━━━━━ 0s 943us/step - accuracy: 0.9761 - loss: 0.1348 Model Accuracy: [0.11227503418922424, 0.9804999828338623]
Y_pred_hyp = best_model_3000.predict(X_test_p_3000)
# Encuentra el índice del valor máximo en cada fila
clases_predichas = np.argmax(Y_pred_hyp, axis=1)
print(clases_predichas)
print(classification_report(Y_test, clases_predichas))
plot_confusion_matrix(y_true=Y_test, y_pred=clases_predichas, classes=unique_labels, normalize=False,
title='Matriz de Confusión')
63/63 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step [0 0 0 ... 4 4 4] precision recall f1-score support 0 0.97 0.97 0.97 400 1 1.00 0.98 0.99 400 2 0.99 0.99 0.99 400 3 0.99 0.99 0.99 400 4 0.96 0.97 0.96 400 accuracy 0.98 2000 macro avg 0.98 0.98 0.98 2000 weighted avg 0.98 0.98 0.98 2000 Confusion matrix, without normalization
<Axes: title={'center': 'Matriz de Confusión'}, xlabel='Predicted label', ylabel='True label'>
# Get the best parameters
best_params_1700 = grid_result.best_params_
# Create a new model using the best parameters
best_model_1700 = create_model_1700(optimizer=best_params_1700['optimizer'], units=best_params_1700['model__units'], activation=best_params_1700['model__activation'])
# Train the best model on the entire training dataset
best_model_1700.fit(X_train_p_1700, Y_train, batch_size=20, epochs=50, verbose=1)
c:\Users\user\BI-Sabroson\Machine-Learning-Labs\env\Lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
Epoch 1/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 0.7420 - loss: 1.0130 Epoch 2/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 977us/step - accuracy: 0.9949 - loss: 0.0243 Epoch 3/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 982us/step - accuracy: 0.9986 - loss: 0.0092 Epoch 4/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.9999 - loss: 0.0034 Epoch 5/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 985us/step - accuracy: 0.9998 - loss: 0.0021 Epoch 6/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 946us/step - accuracy: 0.9998 - loss: 0.0020 Epoch 7/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 966us/step - accuracy: 0.9965 - loss: 0.0114 Epoch 8/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 988us/step - accuracy: 0.9991 - loss: 0.0028 Epoch 9/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 992us/step - accuracy: 0.9989 - loss: 0.0050 Epoch 10/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.9997 - loss: 0.0016 Epoch 11/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 956us/step - accuracy: 0.9973 - loss: 0.0087 Epoch 12/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 944us/step - accuracy: 1.0000 - loss: 8.4803e-04 Epoch 13/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 965us/step - accuracy: 1.0000 - loss: 1.5106e-04 Epoch 14/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 960us/step - accuracy: 1.0000 - loss: 9.4808e-05 Epoch 15/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 961us/step - accuracy: 1.0000 - loss: 6.6610e-05 Epoch 16/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 940us/step - accuracy: 1.0000 - loss: 5.0945e-05 Epoch 17/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 4.3805e-05 Epoch 18/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 3.4928e-05 Epoch 19/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 2.6669e-05 Epoch 20/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 2.1703e-05 Epoch 21/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 1.7050e-05 Epoch 22/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 1.3957e-05 Epoch 23/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 1.0817e-05 Epoch 24/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 989us/step - accuracy: 1.0000 - loss: 8.9911e-06 Epoch 25/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 947us/step - accuracy: 1.0000 - loss: 7.2807e-06 Epoch 26/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 943us/step - accuracy: 1.0000 - loss: 5.9548e-06 Epoch 27/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 4.9761e-06 Epoch 28/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - accuracy: 1.0000 - loss: 4.1877e-06 Epoch 29/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 3.4973e-06 Epoch 30/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 2.6766e-06 Epoch 31/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 2.1968e-06 Epoch 32/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 1.6740e-06 Epoch 33/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 1.3607e-06 Epoch 34/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 940us/step - accuracy: 1.0000 - loss: 1.1463e-06 Epoch 35/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 982us/step - accuracy: 1.0000 - loss: 9.4495e-07 Epoch 36/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 6.9512e-07 Epoch 37/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1000us/step - accuracy: 1.0000 - loss: 5.9057e-07 Epoch 38/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 4.5982e-07 Epoch 39/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 3.7386e-07 Epoch 40/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 2.8465e-07 Epoch 41/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 2.4249e-07 Epoch 42/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 939us/step - accuracy: 1.0000 - loss: 1.9407e-07 Epoch 43/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 953us/step - accuracy: 1.0000 - loss: 1.6290e-07 Epoch 44/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 1.3008e-07 Epoch 45/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 1.0160e-07 Epoch 46/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 8.6538e-08 Epoch 47/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 995us/step - accuracy: 1.0000 - loss: 6.8512e-08 Epoch 48/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 5.7634e-08 Epoch 49/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 4.9685e-08 Epoch 50/50 400/400 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 1.0000 - loss: 4.1679e-08
<keras.src.callbacks.history.History at 0x29a2ebc6d10>
model_accuracy_t_1700 = best_model_1700.evaluate(X_test_p_1700, Y_test)
print("Model Accuracy:", model_accuracy_t_1700)
63/63 ━━━━━━━━━━━━━━━━━━━━ 0s 743us/step - accuracy: 0.9681 - loss: 0.2186 Model Accuracy: [0.1621309369802475, 0.9750000238418579]
Y_pred_hyp = best_model_1700.predict(X_test_p_1700)
# Encuentra el índice del valor máximo en cada fila
clases_predichas = np.argmax(Y_pred_hyp, axis=1)
print(clases_predichas)
print(classification_report(Y_test, clases_predichas))
plot_confusion_matrix(y_true=Y_test, y_pred=clases_predichas, classes=unique_labels, normalize=False,
title='Matriz de Confusión')
63/63 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step [0 0 0 ... 4 4 4] precision recall f1-score support 0 0.96 0.95 0.96 400 1 0.99 0.98 0.99 400 2 0.99 0.99 0.99 400 3 0.99 0.98 0.98 400 4 0.94 0.97 0.96 400 accuracy 0.97 2000 macro avg 0.98 0.97 0.98 2000 weighted avg 0.98 0.97 0.98 2000 Confusion matrix, without normalization
<Axes: title={'center': 'Matriz de Confusión'}, xlabel='Predicted label', ylabel='True label'>
Luego de la búsqueda de hiperparámetros se encontró que el mejor número de componentes principales es de 3000, si bien para las tres búsquedas de hiperparámetros las métricas son muy buenas con 3000 se encontraron los mejores resultados.
Tras la búsqueda de hiperparámetros, se observó una gran mejora en las métricas del modelo en comparación con el algoritmo base y las métricas de este interpretadas de las gráficas. La diferencia es significativa en términos absolutos, los resultados indican un avance importante, ya que el modelo ahora puede predecir todas las categorías con un accuracy bastante elevado.
Además, fue evidente que en el algoritmo base era demasiado complejo para la tarea de clasificación, es por esto que fue necesario no solo necesario reducir la cantidad de neuronas dentro de la capa oculta sino también reducir el número de componentes principales de un 95% a un 83%.
Por otro lado, la categoría de tecnología con la categoría de negocio parecen presentar una muy pequeña confusión de clasificaciones entre ellas, lo que permite al modelo clasificarla con mayor precisión. Este pequeño patrón puede significar que hay palabras que se repiten mucho en ambas categorías, como las marcas de las redes sociales, podría revisarse si es necesario realizar un filtrado de palabras mucho más severo y estricto para identificar.